Learning with Noisy Labels

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Learning with noisy labels asks how models can learn useful decision rules when training targets disagree with the underlying truth, as often happens with crowd annotations, medical labels, and automatically generated supervision. Common approaches include robust losses and noise-transition models, sample selection and reweighting, co-teaching, label correction, and self-supervised or representation-based methods that identify suspicious examples without trusting their labels. Methods are tested on controlled synthetic corruption and real-world benchmarks such as CIFAR and Clothing1M, as well as in medical imaging, graphs, and other domains where noise may depend on the example or annotator. A central challenge is that noise structure and data difficulty matter: small-loss filtering can discard hard but correct examples, and success on closed-set synthetic benchmarks may not transfer to open-set noise or deployment settings without clean labels for evaluation.

Latest papers

151 papers10 papers in the last four weeks, down 29% on the four weeks before. 0.1% of all new papers.

Mar 25, 2026

What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification

Speaker verification at large scale remains an open challenge as fixed-margin losses treat all samples equally regardless of quality. We hypothesize that mislabeled or degraded samples introduce noisy gradients that disrupt compact speaker manifolds. We propose Curry (CURriculum Ranking), an adaptive loss that estimates sample difficulty online via Sub-center ArcFace: confidence scores from dominant sub-center cosine similarity rank samples into easy, medium, and hard tiers using running batch statistics, without auxiliary annotations. Learnable weights guide the model from stable identity foundations through manifold refinement to boundary sharpening. To our knowledge, this is the largest-scale speaker verification system trained to date. Evaluated on VoxCeleb1-O, and SITW, Curry reduces EER by 86.8% and 60.0% over the Sub-center ArcFace baseline, establishing a new paradigm for robust speaker verification on imperfect large-scale data.

Massa Baali, Sarthak Bisht, Rita Singh +1

Carnegie Mellon University, Pittsburgh, USA

Speaker Verification Curriculum Learning Learning with Noisy Labels

Mar 20, 2026

What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time

Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority voting consensus. However, existing TTRL methods rely exclusively on positive pseudo-labeling strategies. Such reliance becomes vulnerable under challenging scenarios where answer distributions are highly dispersed, resulting in weak consensus that inadvertently reinforces incorrect trajectories as supervision signals. In this paper, we propose SCRL (Selective-Complementary Reinforcement Learning), a robust test-time reinforcement learning framework that effectively mitigates label noise amplification. SCRL develops Selective Positive Pseudo-Labeling, which enforces strict consensus criteria to filter unreliable majorities. Complementarily, SCRL introduces Entropy-Gated Negative Pseudo-Labeling, the first negative supervision mechanism in TTRL, to reliably prune incorrect trajectories based on generation uncertainty. Extensive experiments on multiple reasoning benchmarks demonstrate that SCRL achieves substantial improvements over baselines, while maintaining robust generalization and training stability under constrained rollout budgets. Our code is available at https://github.com/Jasper-Yan/SCRL.

Dong Yan, Jian Liang, Yanbo Wang +3

School of Artificial Intelligence, University of Chinese Academy of Sciences · NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences · Nanjing University

RL for Language Model Reasoning Test-Time RL Pseudo-Labeling Learning with Noisy Labels

Mar 20, 2026

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients

Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in distinguishing noisy samples from the dataset for label correction by leveraging loss values. However, noisy samples recognition relying on scalar loss lacks reliability for FL under heterogeneous scenarios. In this paper, we rethink this paradigm from a representation perspective and propose \method~(Federated under Representation Gemometry), which follows the principle of ``representation geometry priority'' to recognize noisy labels. Firstly, \methodcreates label-agnostic spherical representations by using self-supervision. It then iteratively fits a spherical von Mises-Fisher (vMF) mixture model to this geometry using previously identified clean samples to capture semantic clusters. This geometric evidence is integrated with a semantic-label soft mapping mechanism to derive a distribution divergence between the label-free and annotated label-conditioned feature space, which robustly identifies noisy samples and updates the vMF mixture model with the newly separated clean dataset. Lastly, we employ an additional personalized noise absorption matrix on noisy labels to achieve robust optimization. Extensive experimental results demonstrate that \methodsignificantly outperforms state-of-the-art methods for FL with data heterogeneity under diverse noisy clients scenarios.

Tian Wen, Zhiqin Yang, Yonggang Zhang +4

Institute of Computing Technology, Chinese Academy of Sciences · TMLR Group, Hong Kong Baptist University · The Hong Kong University of Science and Technology +1

Federated Learning Learning with Noisy Labels Heterogeneous Federated Learning

Mar 18, 2026

rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks

Neural networks are central to modern artificial intelligence, yet their training remains highly sensitive to data contamination. Standard neural classifiers are trained by minimizing the categorical cross-entropy loss, corresponding to maximum likelihood estimation under a multinomial model. While statistically efficient under ideal conditions, this approach is highly vulnerable to contaminated observations including label noises corrupting supervision in the output space, and adversarial perturbations inducing worst-case deviations in the input space. In this paper, we propose a unified and statistically grounded framework for robust neural classification that addresses both forms of contamination within a single learning objective. We formulate neural network training as a minimum-divergence estimation problem and introduce rSDNet, a robust learning algorithm based on the general class of

S

-divergences. The resulting training objective inherits robustness properties from classical statistical estimation, automatically down-weighting aberrant observations through model probabilities. We establish essential population-level properties of rSDNet, including Fisher consistency, classification calibration implying Bayes optimality, and robustness guarantees under uniform label noise and infinitesimal feature contamination. Experiments on three benchmark image classification datasets show that rSDNet improves robustness to label corruption and adversarial attacks while maintaining competitive accuracy on clean data, Our results highlight minimum-divergence learning as a principled and effective framework for robust neural classification under heterogeneous data contamination.

Suryasis Jana, Abhik Ghosh

Indian Statistical Institute, Kolkata, India

Neural Network Robustness Robust Classification Learning with Noisy Labels Adversarial Robustness Robust Loss Functions

Mar 18, 2026

Variational Rectification Inference for Learning with Noisy Labels

Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (e.g., re-weighting, or loss rectification) have been broadly applied in prevailing approaches, which have been generally learned under the meta-learning scenario. Despite the robustness of noise achieved by the probabilistic meta-learning models, they usually suffer from model collapse that degenerates generalization performance. In this paper, we propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions as an amortized variational inference problem and derive the evidence lower bound under the meta-learning framework. Specifically, VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable, which can rectify the loss of the noisy sample with the extra randomness regularization and is, therefore, more robust to label noise. To achieve the inference of the rectifying vector, we approximate its conditional posterior with an amortization meta-network. By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function, which can significantly improve the generalization performance. The elaborated meta-network and prior network adhere to the smoothness assumption, enabling the generation of reliable rectification vectors. Given a set of clean meta-data, VRI can be efficiently meta-learned within the bi-level optimization programming. Besides, theoretical analysis guarantees that the meta-network can be efficiently learned with our algorithm. Comprehensive comparison experiments and analyses validate its effectiveness for robust learning with noisy labels, particularly in the presence of open-set noise.

Haoliang Sun, Qi Wei, Lei Feng +4

School of Software, Shandong University, Jinan, China. · School of Computer Science and Engineering, Nanyang Technological University, Singapore. · School of Computing, National University of Singapore, Singapore. +1

Learning with Noisy Labels Variational Inference Meta-Learning

Mar 17, 2026

An assessment of data-centric methods for label noise identification in remote sensing data sets

Label noise in the sense of incorrect labels is present in many real-world data sets and is known to severely limit the generalizability of deep learning models. In the field of remote sensing, however, automated treatment of label noise in data sets has received little attention to date. In particular, there is a lack of systematic analysis of the performance of data-centric methods that not only cope with label noise but also explicitly identify and isolate noisy labels. In this paper, we examine three such methods and evaluate their behavior under different label noise assumptions. To do this, we inject different types of label noise with noise levels ranging from 10 to 70% into two benchmark data sets, followed by an analysis of how well the selected methods filter the label noise and how this affects task performances. With our analyses, we clearly prove the value of data-centric methods for both parts - label noise identification and task performance improvements. Our analyses provide insights into which method is the best choice depending on the setting and objective. Finally, we show in which areas there is still a need for research in the transfer of data-centric label noise methods to remote sensing data. As such, our work is a step forward in bridging the methodological establishment of data-centric label noise methods and their usage in practical settings in the remote sensing domain.

Felix Kröber, Genc Hoxha, Ribana Roscher

Institute of Bio- and Geosciences, Forschungszentrum Jülich, Leo-Brandt-Straße, 52425 Jülich, Germany · Institute of Geodesy and Geoinformation, University of Bonn, Germany

Noisy-Label Learning Learning with Noisy Labels Remote Sensing

Mar 15, 2026

Deep EM with Hierarchical Latent Label Modelling for Multi-Site Prostate Lesion Segmentation

Label variability is a major challenge for prostate lesion segmentation. In multi-site datasets, annotations often reflect centre-specific contouring protocols, causing segmentation networks to overfit to local styles and generalise poorly to unseen sites in inference. We treat each observed annotation as a noisy observation of an underlying latent 'clean' lesion mask, and propose a hierarchical expectation-maximisation (HierEM) framework that alternates between: (1) inferring a voxel-wise posterior distribution over the latent mask, and (2) training a CNN using this posterior as a soft target and estimate site-specific sensitivity and specificity under a hierarchical prior. This hierarchical prior decomposes label-quality into a global mean with site- and case-level deviations, reducing site-specific bias by penalising the likelihood term contributed only by site deviations. Experiments on three cohorts demonstrate that the proposed hierarchical EM framework enhances cross-site generalisation compared to state-of-the-art methods. For pooled-dataset evaluation, the per-site mean DSC ranges from 29.50% to 39.69%; for leave-one-site-out generalisation, it ranges from 27.91% to 32.67%, yielding statistically significant improvements over comparison methods (p<0.039). The method also produces interpretable per-site latent label-quality estimates (sensitivity alpha ranges from 31.5% to 47.3% at specificity beta approximates 0.99), supporting post-hoc analyses of cross-site annotation variability. These results indicate that explicitly modelling site-dependent annotation can improve cross-site generalisation.

Wen Yan, Yipei Wang, Shiqi Huang +5

UCL Hawkes Institute; Department of Medical Physics and Biomedical Engineering, University College London, Gower St, London WC1E 6BT, U.K. · Centre for Medical Imaging, Division of Medicine, University College London, Gower St, London WC1E 6BT, U.K. · Division of Surgery and Interventional Science, University College London, 10 Pond St, London NW3 2PS, U.K. +2

Medical Image Segmentation Learning with Noisy Labels Expectation-Maximization Annotator Disagreement

Mar 15, 2026

Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling

Label noise - incorrect labels assigned to observations - can substantially degrade the performance of supervised classifiers. This paper proposes a label noise cleaning method based on Bernoulli random sampling. We show that the mean label noise levels of subsets generated by Bernoulli random sampling containing a given observation are identically distributed for all clean observations, and identically distributed, with a different distribution, for all noisy observations. Although the mean label noise levels are not independent across observations, by introducing an independent coupling we further prove that they converge to a mixture of two well-separated distributions corresponding to clean and noisy observations. By establishing a linear model between cross-validated classification errors and label noise levels, we are able to approximate this mixture distribution and thereby separate clean and noisy observations without any prior label information. The proposed method is classifier-agnostic, theoretically justified, and demonstrates strong performance on both simulated and real datasets.

Yuxin Liu, Xiong Jin, Yang Han

Department of Mathematics, The University of Manchester, UK · Department of Mathematics, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Learning with Noisy Labels Noisy-Label Learning

Mar 14, 2026

Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels

Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean dataset, our method employs noisy posterior simulation and intermediate features to transfer ground-truth knowledge, forming a closed-loop feedback system that prevents error amplification. Theoretical guarantees underpin the stability of our approach, and extensive experiments on benchmark datasets like CIFAR and Clothing1M confirm state-of-the-art performance with reduced training time, highlighting its practical applicability for learning from noisy labels.

Zhanhui Lin, Yanlin Liu, Sanping Zhou

Noisy-Label Learning Learning with Noisy Labels

Mar 13, 2026

Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis

Statistically consistent methods based on the noise transition matrix (

T

) offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of convergence to the optimal clean-data classifier. In practice, however, these methods are often outperformed by empirical approaches such as sample selection, and this gap is usually attributed to the difficulty of accurately estimating

T

. The common assumption is that, given a perfect

T

, noise-correction methods would recover their theoretical advantage. In this work, we put this longstanding hypothesis to a decisive test. We conduct experiments under idealized conditions, providing correction methods with a perfect, oracle transition matrix. Even under these ideal conditions, we observe that these methods still suffer from performance collapse during training. This compellingly demonstrates that the failure is not fundamentally a

T

-estimation problem, but stems from a more deeply rooted flaw. To explain this behaviour, we provide a unified analysis that links three levels: macroscopic convergence states, microscopic optimisation dynamics, and information-theoretic limits on what can be learned from noisy labels. Together, these results give a formal account of why ideal noise correction fails and offer concrete guidance for designing more reliable methods for learning with noisy labels.

Chen Feng, Zhuo Zhi, Zhao Huang +5

Queen’s University Belfast · University College London · University of Aberdeen +3

Learning with Noisy Labels Statistical Learning Theory

Mar 12, 2026

Real-World Point Tracking with Verifier-Guided Pseudo-Labeling

Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due to different characteristics and the absence of dense ground-truth annotations. Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher models, which vary across frames and scenes. In this paper, we address the problem of real-world fine-tuning and introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation. Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions, resulting in high-quality pseudo-label trajectories. When applied for fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos. Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods. Project page: https://kuis-ai.github.io/track_on_r

Görkay Aydemir, Fatma Güney, Weidi Xie

Department of Computer Engineering, Koc¸ University · KUIS AI Center · School of Artificial Intelligence, Shanghai Jiao Tong University

Point Tracking Pseudo-Labeling Unsupervised Domain Adaptation Learning with Noisy Labels

Mar 11, 2026

On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD

One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we delve into the underlying mechanisms behind stochastic gradient descent (SGD) with label noise. Focusing on a two-layer over-parameterized linear network, we analyze the learning dynamics of label noise SGD, unveiling a two-phase learning behavior. In Phase I, the magnitudes of model weights progressively diminish, and the model escapes the lazy regime; enters the rich regime. In Phase II, the alignment between model weights and the ground-truth interpolator increases, and the model eventually converges. Our analysis highlights the critical role of label noise in driving the transition from the lazy to the rich regime and minimally explains its empirical success. Furthermore, we extend these insights to Sharpness-Aware Minimization (SAM), showing that the principles governing label noise SGD also apply to broader optimization algorithms. Extensive experiments, conducted under both synthetic and real-world setups, strongly support our theory. Our code is released at https://github.com/a-usually/Label-Noise-SGD.

Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang +4

Sch. of Computer Science and Zhiyuan College, Shanghai Jiao Tong University · Peking University · University of Sydney +4

Two-Layer Neural Networks Neural Network Training Dynamics Stochastic Gradient Descent Learning with Noisy Labels

Mar 9, 2026

DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning

In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained supervision problems inherent in Outcome Reward Models (ORMs), their deployment is hindered by the prohibitive cost of obtaining expert-verified step-wise labels. This paper addresses the challenge of training reliable PRMs using abundant but noisy "weak" supervision. We argue that existing Weak-to-Strong Generalization (W2SG) theories lack prescriptive guidelines for selecting high-quality training signals from noisy data. To bridge this gap, we introduce the Dual-Consensus Weak-to-Strong (DC-W2S) framework. By intersecting Self-Consensus (SC) metrics among weak supervisors with Neighborhood-Consensus (NC) metrics in the embedding space, we stratify supervision signals into distinct reliability regimes. We then employ a curriculum of instance-level balanced sampling and label-level reliability-aware masking to guide the training process. We demonstrate that DC-W2S enables the training of robust PRMs for complex reasoning without exhaustive expert annotation, proving that strategic data curation is more effective than indiscriminate training on large-scale noisy datasets.

Chi-Min Chan, Ehsan Hajiramezanali, Xiner Li +6

The Hong Kong University of Science and Technology, Hong Kong · Genentech, Inc., South San Francisco, CA, USA.

Process Reward Models Weak-to-Strong Generalization Weakly Supervised Learning Learning with Noisy Labels Scientific Reasoning in Language Models Scientific Reasoning

Mar 6, 2026

DiffInf: Influence-Guided Diffusion for Supervision Alignment in Facial Attribute Learning

Facial attribute classification relies on large-scale annotated datasets in which many traits, such as age and expression, are inherently ambiguous and continuous but are discretized into categorical labels. Annotation inconsistencies arise from subjectivity and visual confounders such as pose, illumination, expression, and demographic variation, creating mismatch between images and assigned labels. These inconsistencies introduce supervision errors that impair representation learning and degrade downstream prediction. We introduce DiffInf, a self-influence--guided diffusion framework for mitigating annotation inconsistencies in facial attribute learning. We first train a baseline classifier and compute sample-wise self-influence scores using a practical first-order approximation to identify training instances that disproportionately destabilize optimization. Instead of discarding these influential samples, we apply targeted generative correction via a latent diffusion autoencoder to better align visual content with assigned labels while preserving identity and realism. To enable differentiable guidance during correction, we train a lightweight predictor of high-influence membership and use it as a surrogate influence regularizer. The edited samples replace the originals, yielding an influence-refined dataset of unchanged size. Across multi-class facial attribute classification, DiffInf consistently improves generalization compared with standard noisy-label training, robust optimization baselines, and influence-based filtering. Our results demonstrate that repairing influential annotation inconsistencies at the image level enhances downstream facial attribute classification without sacrificing distributional coverage.

Basudha Pal, Zhaoyang Wang, Rama Chellappa

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA · Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA · Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

Learning with Noisy Labels Image Classification

Mar 6, 2026

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise

3D semantic occupancy prediction is a cornerstone of robotic perception, yet real-world voxel annotations are inherently corrupted by structural artifacts and dynamic trailing effects. This raises a critical but underexplored question: can autonomous systems safely rely on such unreliable occupancy supervision? To systematically investigate this issue, we establish OccNL, the first benchmark dedicated to 3D occupancy under occupancy-asymmetric and dynamic trailing noise. Our analysis reveals a fundamental domain gap: state-of-the-art 2D label noise learning strategies collapse catastrophically in sparse 3D voxel spaces, exposing a critical vulnerability in existing paradigms. To address this challenge, we propose DPR-Occ, a principled label-noise-robust framework that constructs reliable supervision through dual-source partial label reasoning. By synergizing temporal model memory with representation-level structural affinity, DPR-Occ dynamically expands and prunes candidate label sets to preserve true semantics while suppressing noise propagation. Extensive experiments on SemanticKITTI demonstrate that DPR-Occ prevents geometric and semantic collapse under extreme corruption. Notably, even at 90% label noise, our method achieves significant performance gains (up to 2.57% mIoU and 13.91% IoU) over existing label noise learning baselines adapted to the 3D occupancy prediction task. By bridging label noise learning and 3D perception, OccNL and DPR-Occ provide a reliable foundation for safety-critical robotic perception in dynamic environments. The benchmark and source code will be made publicly available at https://github.com/mylwx/OccNL.

Wenxin Li, Kunyu Peng, Di Wen +6

School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China · Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany · INSAIT, Sofia University “St. Kliment Ohridski”, Bulgaria +1

3D Occupancy Prediction Learning with Noisy Labels

Mar 2, 2026

Spectral Overfitting in Noisy Linear Probing of Pretrained Representations

Frozen pretrained features are often treated as a safe interface for downstream learning: only a small linear readout is trained, while the backbone is fixed. We show that this readout can still overfit noisy labels in a structured way. A label-blind PCA rank sweep reveals a sharp spectral pattern: under label noise, exposing all pretrained directions can hurt clean accuracy, and intermediate ranks often recover much of the lost performance. Rank-matched random projections help less, and measured between-class signal is strongly concentrated in leading PCs. The pattern appears across three ImageNet-pretrained backbones on CIFAR-10, with gains up to

36.0\pm0.8

points over the default full-rank probe at 40% noise. Tuned full-rank probes outperform validation-selected PCA probes, so we present the sweep as a diagnostic of spectral overfitting rather than a competitive noisy-label method.

Zice Wang, Zhenyu Zhang

School of Future Technology, Northeastern University, Shenyang, China.

Representation Learning Linear Probing Learning with Noisy Labels

Feb 28, 2026

Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation

High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise due to the labor-intensive and time-consuming nature of pixel-wise annotation, which makes it challenging for human annotators to label every pixel accurately. Annotation errors can significantly degrade the performance and robustness of modern segmentation models, motivating the need for reliable mechanisms to identify and quantify noisy training samples. This paper introduces a novel data-centric benchmark, together with a new, publicly available binary building segmentation dataset. This specific task serves as a representative and practically relevant testbed that enables controlled experimentation with different annotation perturbations. Furthermore, we also introduce two techniques for identifying, quantifying, and ranking training samples according to their level of label noise in remote sensing semantic segmentation. Such proposed methods leverage complementary strategies based on model uncertainty, prediction consistency, and representation analysis, and consistently outperform established baselines across a range of experimental settings. The outcomes of this work are publicly available at https://github.com/keillernogueira/label_noise_segmentation.

Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9

School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 7ZX, UK · German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling, 82234, Germany · Division of Geoinformatics, KTH Royal Institute of Technology, Stockholm, 114 28, Sweden +4

Learning with Noisy Labels Noisy-Label Learning Remote Sensing Benchmarks Remote Sensing Image Segmentation

Feb 26, 2026

Human Supervision as an Information Bottleneck: A Unified Theory of Error Floors in Human-Guided Learning

Large language models are trained primarily on human-generated data and feedback, yet they exhibit persistent errors arising from annotation noise, subjective preferences, and the limited expressive bandwidth of natural language. We argue that these limitations reflect structural properties of the supervision channel rather than model scale or optimization. We develop a unified theory showing that whenever the human supervision channel is not sufficient for a latent evaluation target, it acts as an information-reducing channel that induces a strictly positive excess-risk floor for any learner dominated by it. We formalize this Human-Bounded Intelligence limit and show that across six complementary frameworks (operator theory, PAC-Bayes, information theory, causal inference, category theory, and game-theoretic analyses of reinforcement learning from human feedback), non-sufficiency yields strictly positive lower bounds arising from the same structural decomposition into annotation noise, preference distortion, and semantic compression. The theory explains why scaling alone cannot eliminate persistent human-aligned errors and characterizes conditions under which auxiliary non-human signals (e.g., retrieval, program execution, tools) increase effective supervision capacity and collapse the floor by restoring information about the latent target. Experiments on real preference data, synthetic known-target tasks, and externally verifiable benchmarks confirm the predicted structural signatures: human-only supervision exhibits a persistent floor, while sufficiently informative auxiliary channels strictly reduce or eliminate excess error.

Alejandro Rodriguez Dominguez

Miralta Finance Bank S.A., Spain · Department of Computer Science, University of Reading, UK

Information Bottleneck Learning with Noisy Labels RL from Human Feedback

Feb 26, 2026

From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification

With the increasing demand for robust person Re-ID in unconstrained environments, learning from datasets with noisy labels and sparse per-identity samples remains a critical challenge. Existing noise-robust person Re-ID methods primarily rely on loss-correction or sample-selection strategies using softmax outputs. However, these methods suffer from two key limitations: 1) Softmax exhibits translation invariance, leading to over-confident and unreliable predictions on corrupted labels. 2) Conventional sample selection based on small-loss criteria often discards valuable hard positives that are crucial for learning discriminative features. To overcome these issues, we propose the CAlibration-to-REfinement (CARE) method, a two-stage framework that seeks certainty through probabilistic evidence propagation from calibration to refinement. In the calibration stage, we propose the probabilistic evidence calibration (PEC) that dismantles softmax translation invariance by injecting adaptive learnable parameters into the similarity function, and employs an evidential calibration loss to mitigate overconfidence on mislabeled samples. In the refinement stage, we design the evidence propagation refinement (EPR) that can more accurately distinguish between clean and noisy samples. Specifically, the EPR contains two steps: Firstly, the composite angular margin (CAM) metric is proposed to precisely distinguish clean but hard-to-learn positive samples from mislabeled ones in a hyperspherical space; Secondly, the certainty-oriented sphere weighting (COSW) is developed to dynamically allocate the importance of samples according to CAM, ensuring clean instances drive model updates. Extensive experimental results on Market1501, DukeMTMC-ReID, and CUHK03 datasets under both random and patterned noises show that CARE achieves competitive performance.

Xin Yuan, Zhiyong Zhang, Xin Xu +2

School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430065, China · Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430065, China · School of Computer Science, Wuhan University, Wuhan, Hubei 430072, China +1

Person Re-Identification Learning with Noisy Labels Noisy-Label Learning Evidential Deep Learning

Feb 25, 2026

Testable Learning of General Halfspaces under Massart Noise

We study the algorithmic task of testably learning general Massart halfspaces under the Gaussian distribution. In the testable learning setting, the aim is the design of a tester-learner pair satisfying the following properties: (1) if the tester accepts, the learner outputs a hypothesis and a certificate that it achieves near-optimal error, and (2) it is highly unlikely that the tester rejects if the data satisfies the underlying assumptions. Our main result is the first testable learning algorithm for general halfspaces with Massart noise and Gaussian marginals. The complexity of our algorithm is

d^{\mathrm{polylog}(\min\{1/γ, 1/ε\})}

, where

ε

is the excess error and

γ

is the bias of the target halfspace, which qualitatively matches the known quasi-polynomial Statistical Query lower bound for the non-testable setting. The analysis of our algorithm hinges on a novel sandwiching polynomial approximation to the sign function with multiplicative error that may be of broader interest.

Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane +1

UW Madison · UC San Diego

Halfspace Learning Learning with Noisy Labels Noisy-Label Learning

Feb 24, 2026

Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise

Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given

k

data sources, the goal is to output a classifier for each source by exploiting shared structure to reduce sample complexity. We focus on the bounded label noise setting to determine whether the fast

1/ε

rates achievable in single-task learning extend to this regime with minimal dependence on

k

. Surprisingly, we show that this is not the case. We demonstrate that learning across

k

distributions inherently incurs slow rates scaling with

k/ε^2

, even under constant noise levels, unless each distribution is learned separately. A key technical contribution is a structured hypothesis-testing framework that captures the statistical cost of certifying near-optimality under bounded noise-a cost we show is unavoidable in the multi-distribution setting. Finally, we prove that when competing with the stronger benchmark of each distribution's optimal Bayes error, the sample complexity incurs a multiplicative penalty in

k

. This establishes a statistical separation between random classification noise and Massart noise, highlighting a fundamental barrier unique to learning from multiple sources.

Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet

MIT

Statistical Learning Theory Learning with Noisy Labels

Feb 23, 2026

Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems

Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational data and physical constraints in a unified optimization objective. However, the ill-posed nature of PDE inverse problems makes them highly sensitive to noise. Even a small fraction of corrupted observations can distort internal neural representations, severely impairing accuracy and destabilizing training. Motivated by recent advances in machine unlearning and structured network pruning, we propose P-PINN, a selective pruning framework designed to unlearn the influence of corrupted data in a pretrained PINN. Specifically, starting from a PINN trained on the full dataset, P-PINN evaluates a joint residual--data fidelity indicator, a weighted combination of data misfit and PDE residuals, to partition the training set into reliable and corrupted subsets. Next, we introduce a bias-based neuron importance measure that quantifies directional activation discrepancies between the two subsets, identifying neurons whose representations are predominantly driven by corrupted samples. Building on this, an iterative pruning strategy then removes noise-sensitive neurons layer by layer. The resulting pruned network is fine-tuned on the reliable data subject to the original PDE constraints, acting as a lightweight post-processing stage rather than a complete retraining. Numerical experiments on extensive PDE inverse-problem benchmarks demonstrate that P-PINN substantially improves robustness, accuracy, and training stability under noisy conditions, achieving up to a 96.6% reduction in relative error compared with baseline PINNs. These results indicate that activation-level post hoc pruning is a promising mechanism for enhancing the reliability of physics-informed learning in noise-contaminated settings.

Yongsheng Chen, Yong Chen, Wei Guo +1

School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China. · Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA. · Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 70409, USA.

Physics-Informed ML PDE Inverse Problems Neural Network Pruning Learning with Noisy Labels Machine Unlearning

Feb 18, 2026

Learning under noisy supervision is governed by a feedback-truth gap

When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out labels, the objectively correct option, or the participant's pre-feedback expectation - the only non-circular reference decodable from post-feedback EEG. The gap appeared universally but was regulated differently: dense networks accumulated it as memorization; sparse-residual scaffolding suppressed it; humans generated transient over-commitment that was actively recovered. Neural over-commitment (~0.04-0.10) was amplified tenfold into behavioral commitment (d = 3.3-3.9). The gap is a fundamental constraint on learning under noisy supervision; its consequences depend on the regulation each system employs.

Elan Schonfeld, Elias Wisnia

Department of Biology, Columbia University, New York, NY, USA · Department of Chemistry, Columbia University, New York, NY, USA

Learning with Noisy Labels Neural Network Training Dynamics

Feb 6, 2026

Reliable Mislabel Detection for Video Capsule Endoscopy Data

The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however, obtaining such datasets is particularly challenging since annotations must be provided by specialized physicians, which severely limits the pool of annotators. Furthermore, class boundaries can often be ambiguous or difficult to define which further complicates machine learning-based classification. In this paper, we want to address this problem and introduce a framework for mislabel detection in medical datasets. This is validated on the two largest, publicly available datasets for Video Capsule Endoscopy, an important imaging procedure for examining the gastrointestinal tract based on a video stream of lowresolution images. In addition, potentially mislabeled samples identified by our pipeline were reviewed and re-annotated by three experienced gastroenterologists. Our results show that the proposed framework successfully detects incorrectly labeled data and results in an improved anomaly detection performance after cleaning the datasets compared to current baselines.

Julia Werner, Julius Oexle, Oliver Bause +5

Embedded Systems University of Tübingen Tübingen, Germany · Department of Medicine I University Hospital Dresden TU Dresden, Germany

Endoscopy Medical Imaging Training Data Curation Learning with Noisy Labels

Dec 14, 2025

Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are often noisy due to varying labeler expertise and annotation uncertainty, especially for complex or ambiguous samples. Learning from such imperfectly labeled data can degrade classifier performance. We propose an AL framework that explicitly accounts for label noise by optimally assigning labelers and selecting samples to minimize labeling error. Our approach, called OLAS (Optimal Labeler Assignment and Sampling), uses a noise model that depends on both labeler accuracy and model uncertainty to guide these decisions. We develop two tractable optimization formulations: one for assigning samples to labelers to minimize worst-case noise, and another for selecting samples while controlling overall label noise. Theoretical results provide closed-form solutions under mild conditions. Empirical evaluations on benchmark datasets and a real-world warranty claim classification problem show that OLAS achieves the highest or near-highest classification accuracy among existing AL strategies across most settings, using only a single label per sample.

Pouya Ahadi, Blair Winograd, Camille Zaug +3

School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA · Ford Motor Company

Human-in-the-Loop Annotation Active Learning Learning with Noisy Labels

Nov 25, 2025

Pre-train to Gain: Robust Learning Without Clean Labels

Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approaches for learning with noisy labels often rely on the availability of a clean subset of data. By pre-training a feature extractor on the target dataset without labels using in-domain self-supervised learning (SSL), followed by standard supervised training on the same noisy dataset, we can train a more noise robust model without requiring a subset with clean labels. We evaluate both contrastive and non-contrastive SSL pre-training methods across datasets with synthetic and real-world label noise, demonstrating the broad applicability of our approach across large-scale datasets, diverse downstream tasks, and model architectures. Across all noise rates, in-domain self-supervised pre-training consistently improves classification accuracy and downstream label-error detection (F1 and Balanced Accuracy) compared with supervised training from scratch. The performance gap widens as the noise rate increases, demonstrating improved robustness. Notably, our approach achieves comparable results to ImageNet and DinoV2 pre-trained models at low noise levels, while substantially outperforming them under high noise conditions.

David Szczecina, Nicholas Pellegrino, Paul Fieguth

Systems Design Engineering University of Waterloo Waterloo, Ontario, Canada

Self-Supervised Pre-Training Learning with Noisy Labels

Sep 18, 2025

Efficient Conformal Prediction for Regression Models under Label Noise

In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals. Recently, Conformal Prediction (CP) has emerged as a powerful statistical framework that, based on a labeled calibration set, generates intervals that include the true labels with a pre-specified probability. In this paper, we address the problem of applying CP for regression models when the calibration set contains noisy labels. We begin by establishing a mathematically grounded procedure for estimating the noise-free CP threshold. Then, we turn it into a practical algorithm that overcomes the challenges arising from the continuous nature of the regression problem. We evaluate the proposed method on two medical imaging regression datasets with Gaussian label noise. Our method significantly outperforms the existing alternative, achieving performance close to the clean-label setting.

Yahav Cohen, Jacob Goldberger, Tom Tirer

Faculty of Engineering Bar-Ilan University Ramat Gan, Israel

Conformal Prediction Learning with Noisy Labels Regression Prediction Intervals

May 27, 2025

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets

Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models, we investigate the relationship between label quality and model performance. Expanding beyond models trained directly for inference, we conduct the first study isolating the impact of label quality in pre-training datasets. While high-quality labels remain essential for models proceeding directly to deployment, we find no evidence that strict label quality is crucial for pre-training efficacy. These results question the necessity of exhaustive human-in-the-loop refinement for massive corpora intended for pretraining and suggest that expert effort is more effectively invested in well-curated downstream target datasets.

Alexander Jaus, Zdravko Marinov, Constantin Seibold +4

Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany · Diagnostic and Interventional Radiology, Heidelberg University Hospital +1

Image Segmentation Medical Imaging Foundation Models Healthcare Medical Image Segmentation Learning with Noisy Labels Medical Image Representation Learning

May 29, 2024

Hierarchical Bayesian Crowdsourcing with Item Difficulty

In applied statistics and machine learning, the gold standards used for training are often biased and almost always noisy. Dawid and Skene's justifiably popular crowdsourcing model adjusts for rater sensitivity and specificity, but fails to capture distributional properties of rating data gathered for training, which in turn biases training. In this study, we introduce a general purpose measurement-error model with which we can infer consensus categories by adding item-level effects for difficulty, discriminativeness, and guessability. We further show how to constrain the bimodal posterior of these models to avoid adversarial raters. We validate our model's goodness of fit with posterior predictive checks, the Bayesian analogue of

χ^2

tests, and assess its predictive accuracy using leave-one-out cross-validation. We illustrate our new model with two well-studied data sets, binary rating data for caries in dental X-rays and implication in natural language.

Seong Woo Han, Ozan Adıgüzel, Bob Carpenter

University of Pennsylvania · Columbia University · Center for Computational Mathematics, Flatiron Institute

Inter-Rater Reliability Latent Variable Models Bayesian Inference Hierarchical Bayesian Modeling Learning with Noisy Labels

Feb 9, 2023

The Re-Label Method For Data-Centric Machine Learning

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The dev dataset evaluation results and human evaluation results verify our idea.

Tong Guo

Learning with Noisy Labels Human-in-the-Loop Annotation