OOD Generalization

OOD: Out-of-Distribution

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29 papers in the last four weeks, up 263% on the four weeks before. 0.3% of all new papers.

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SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track
May 14, 2026Lukas Roming, Felix Lehnerer, Jonas V. Funk +4OOD Generalization Industrial Anomaly Detection
Fraunhofer IOSB, Fraunhoferstr. 1, 76131 Karlsruhe, Germany · Independent Research
TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability
May 14, 2026Krish Sharma, Omar Naim, Soumadeep Saha +3Representation Geometry LLM Pruning
ANITI, France · Meta · Apple, USA
GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction
May 13, 2026Yifan Duan, Siyuan Zheng, Lihuan Li +2Time Series Forecasting OOD Generalization
School of Computer Science and Engineering University of New South Wales
Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization
May 13, 2026Zhuohao Lin, Kun Li, Jiameng Chen +4Unsupervised Domain Adaptation Domain Adaptation
School of Computer Science Wuhan University Wuhan, China · College of Computer Science and Technology, Zhejiang University Hangzhou, China · School of Life Sciences and Technology, Tongji University Shanghai, 200092, China +1
Learning Perturbations to Extrapolate Your LLM
May 13, 2026Zetai Cen, Chenfei Gu, Jin Zhu +3OOD Generalization Prompt Tuning
School of Mathematics, University of Bristol · School of Statistics and Data Science, Shanghai University of Finance and Economics · School of Mathematics, University of Birmingham +1
A $_3$ B $_2$ : Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning
May 13, 2026Yiyun Zhou, Zhonghua Jiang, Wenkang Han +4Vision-Language Models VLM Adaptation
1Zhejiang University · 2Swansea University
Frequency Bias and OOD Generalization in Neural Operators under a Variable-Coefficient Wave Equation
May 13, 2026Runlong Xie, An LuoFourier Neural Operator PDE Solving
Independent Researcher · School of Statistics, University of Minnesota, MN, USA.
Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model
May 13, 2026Hongmin LiLogistic Regression OOD Generalization
School of Life Science and Technology, Institute of Science Tokyo · Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
Spectral Gradient Surgery for Domain-Generalizable Dataset Distillation
May 13, 2026Minyoung Oh, Najeong Chae, Jae-Young SimOOD Generalization Distribution Matching
Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology (UNIST) Ulsan, Republic of Korea
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
May 12, 2026Nishad Singhi, Christian Bialas, Snehal Jauhri +4OOD Generalization Embodied AI
Technical University of Darmstadt & hessian.AI
PyroAdapt: Adapting Wildfire Prediction under Spatial Heterogeneity and Temporal Shift
May 12, 2026Enyi Jiang, Wu SunLearning to Rank Domain Adaptation
Siebel School of Computing and Data Science, University of Illiois at Urbana-Champaign · Computer Science, Stanford University · Department of Global Ecology, Carnegie Institution for Science
A Controlled Counterexample to Strong Proxy-Based Explanations of OOD Performance: in a Fixed Pretraining-and-Probing Setup
May 12, 2026Hongmin LiOOD Generalization Language Model Probing
School of Life Science and Technology, Institute of Science Tokyo · Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
May 11, 2026Yonghan Yang, Ye Yuan, Zipeng Sun +5Surrogate Modeling Surrogate-Assisted Optimization
MBZUAI - Mohamed bin Zayed University of Artificial Intelligence · McGill University · Mila - Quebec AI Institute +1
DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
May 11, 2026Yongsen Tan, Zhecheng Sheng, Xiruo Ding +2Distribution Shift Robustness OOD Generalization
University of Washington · University of Minnesota
PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models
May 11, 2026Xinyu Guo, Bin Xie, Wei Chai +4Robot Policy Adaptation Robot Skill Learning
Institute of Automation, Chinese Academy of Sciences · Dexmal · Nanjing University of Aeronautics and Astronautics +1
Counterfactual Stress Testing for Image Classification Models
May 11, 2026Moritz Stammel, Fabio De Sousa Ribeiro, Raghav Mehta +2Distribution Shift Robustness OOD Generalization
Department of Computing, Imperial College London, UK
On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
May 11, 2026Sam Money-Kyrle, Markus Dablander, Thierry Hanser +3Graph Neural Networks OOD Generalization
Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford, OX1 3LB, United Kingdom. · Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Road, Oxford, OX2 6GG, United Kingdom. · Molecular Informatics and AI, Lhasa Limited, Granary Wharf House,May 2 Canal Wharf, Leeds, LS11 5PS, United Kingdom.
QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space
May 11, 2026Pablo Martínez Crespo, Stefano Ribes, Martin Rahm +6Graph Neural Networks Quantum Chemistry
Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg · Department of Chemistry and Chemical Engineering Chalmers University of Technology · Technology Research Intel Corporation +3
Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
May 10, 2026S. A. Shteingolts, Salman N. Salman, Dan MendelsGraph Neural Networks OOD Generalization
The Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Do Linear Probes Generalize Better in Persona Coordinates?
May 10, 2026Prasad Mahadik, Adrians SkaparsLanguage Model Safety Evaluation Deception in Language Models
Independent Researcher · University of Manchester
Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
May 10, 2026Ruiying Peng, Mengyu Yang, Jing Lei +3Supervised Fine-Tuning Memorization in Language Models
Tsinghua Shenzhen International Graduate School · Huawei Technologies
Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
May 9, 2026Amir Masoud Nourollah, Irtaza Khalid, Stefano Leoni +1Machine Learning Interatomic Potentials OOD Generalization
Cardiff University Cardiff, CF10 3AT, UK
ACWM-Phys: Investigating Generalized Physical Interaction in Action-Conditioned Video World Models
May 9, 2026Haotian Xue, Yipu Chen, Liqian Ma +4Action-Conditioned Video Generation World Models
Georgia Institute of Technology
Rotation-Preserving Supervised Fine-Tuning
May 8, 2026Hangzhan Jin, Tianwei Ni, Lu Li +3Supervised Fine-Tuning Fine-Tuning
1Mila - Quebec AI Institute · 2Polytechnique Montréal · 3Université de Montréal +3
A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds
May 8, 2026Benjamin D. Shaffer, Brooks Kinch, M. Ani Hsieh +1Point Cloud Learning PDE Surrogate Modeling
University of Pennsylvania Philadelphia, PA 19104 · *Mechanical Engineering and Applied Mechanics
ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles
May 8, 2026Thomas Gorges, Janne van der Loop, Lukas Hüttner +4Biometric Identification OOD Generalization
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Buchwissenschaft, Johannes Gutenberg-Universität Mainz, Germany
Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints
May 8, 2026Tsuyoshi OkitaOOD Generalization Physics-Informed ML
Kyushu Institute of Technology
Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization
May 8, 2026Leonel Aguilar, Jan Nagler, Christoph Hoelscher +1OOD Generalization Inductive Bias
Chair of Cognitive Science, ETH Zürich · Centre for Human and Machine Intelligence, Frankfurt School · Aisot Technologies AG +1
Offline Policy Optimization with Posterior Sampling
May 8, 2026Hongqiang Lin, Dongxu Zhang, Yiding Sun +3OOD Generalization Posterior Sampling
Zhejiang University · Xi’an Jiaotong University · Institute of Automation, Chinese Academy of Sciences +1
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Meta-Learning OOD Generalization
Department of ECE and INMC, Seoul National University, Seoul, South Korea · Graduate School of Data Science, Seoul National University, Seoul, South Korea · Department of Computer Science, University of Colorado Boulder, CO, USA

May 14, 2026, cs.CVJ/K move · Enter open · S save

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track

Lukas Roming, Felix Lehnerer, Jonas V. Funk, Andreas Michel+3

Fraunhofer IOSB, Fraunhoferstr. 1, 76131 Karlsruhe, Germany · Independent Research

OOD Generalization Industrial Anomaly Detection Anomaly Localization

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Visual anomaly detection (AD) for industrial inspection is a highly relevant task in modern production environments. The problem becomes particularly challenging when training and deployment data differ due to changes in acquisition conditions during production. In the VAND 4.0 Industrial Track, models must remain robust under distribution shifts such as varying illumination and their performance is assessed on the MVTec AD 2 dataset. To address this setting, we propose a training-free and class-agnostic anomaly detection pipeline based on the work of SuperAD. Our approach improves generalization through several modifications designed to enhance robustness under distribution shifts. These adaptations include using a DINOv3 backbone, overlapping patch-wise processing, intensity-based augmentations, improved memory-bank subsampling for better coverage of the data distribution, and iterative morphological closing for cleaner and more spatially consistent anomaly maps. Unlike methods that rely on class-specific architectures or per-class hyperparameter tuning, our method uses a single architecture and one shared hyperparameter configuration across all object classes. This makes the approach well suited for industrial deployment, where product variants and appearance changes must be handled with minimal adaptation effort. We achieve segmentation F1 scores of

62.61\%

57.42\%

, and

54.35\%

on test public, private, and private mixed of MVTec AD 2 respectively, thereby outperforming SuperAD and other state-of-the-art methods. Code is available at https://github.com/LukasRoom/SuperADD.

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Latest papers 261