Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification as separate, sequential stages: first enhancing the signal and then applying a classifier. This approach fails to leverage the semantic information in the classifier's output during denoising. In this work, we propose a general, domain-agnostic framework that integrates two interacting diffusion models: one operating on the input signal and the other on the classifier's output logits, without requiring any retraining or fine-tuning of the classifier. This coupled formulation enables mutual guidance, where the enhancing signal refines the class estimation and, conversely, the evolving class logits guide the signal reconstruction towards discriminative regions of the manifold. We introduce three strategies to effectively model the joint distribution of the input and the logit. We evaluated our joint enhancement method for image classification and automatic speech recognition. The proposed framework surpasses traditional sequential enhancement baselines, delivering robust and flexible improvements in classification accuracy under diverse noise conditions.
Figures & tables
Figure 1: Comparison of noisy signal classification paradigms. xcor , ycor denote the corrupted signal and logits; x^0 , y^0 their denoised versions. (a) Noisy: direct classification. (b) Enhanced: independent signal enhancement followed by classification. (c) CARD: denoising logits, y , given fixed corrupted signal, xcor . (d) Proposed: coupled denoising of both signal and logits.
Figure 2: Overview of the proposed architectures. (a) Parallel : x and y are denoised simultaneously, exchanging estimates x^0 and y^0 at each timestep t for step-wise cross-conditioning. (b) Alternating : an iterative refinement strategy where complete diffusion trajectories for x and y condition each other in alternating turns. (c) Nested : a guidance mechanism where every single step of the outer y process triggers a full inner diffusion loop over x .
Figure 3: Qualitative comparison under 30% GN. The enhanced baseline incorrectly recognizes “2,” whereas our coupled strategies ( Alternating , Parallel , and Nested ) correctly recover the digit by leveraging logit-space guidance, reconstructing the missing upper-left stroke, enabling correct recognition as “8.”
Baselines (%) ↑
Coupled (Ours) (%) ↑
Dataset
Corruption
Noisy
Enhanced
URIE
CARD
Parallel
Nested
Alternating
MNIST
15% GN
89.7
98.4
99.4
86.3
99.2
99.3
98.9
30% GN
66.7
98.3
99.3
66.2
99.2
99.1
98.7
Gaussian Blur
89.6
98.3
99.5
87.0
99.2
99.3
98.6
CIFAR-10
15% GN
16.6
60.9
68.5
11.3
74.3
81.3
83.0
30% GN
10.9
60.4
59.4
10.4
71.6
81.2
82.8
Table 1: Classification accuracy (%) on image datasets, with pre-trained classifier (Noisy), Enhanced signal baseline, URIE (enhancer trained with regression and classification losses), static conditional generation of logits (CARD), and our coupled strategies. Best robust results are highlighted in bold.
Baselines (%) ↓
Coupled (Ours) (%) ↓
Dataset
Noisy
Enhanced
Dissen
CARD
Parallel
Nested
Alternating
G.Cmds-Reverb
5.58
5.11
7.96
11.08
4.98
4.85
5.03
EARS-Reverb
5.79
4.48
6.46
16.79
3.95
3.83
4.25
EARS-WHAM
14.02
10.04
13.04
36.58
9.90
9.75
10.16
Table 2: WER (%) comparison on Google Commands, EARS-Reverb and EARS-WHAM. Noisy denotes the performance of Whisper-base. Enhanced denotes audio-only enhancement, Dissen denotes an enhancer trained with regression and classification losses, and CARD denotes logits-only enhancement.
Figure 4: Robustness vs. Sampling Steps. Accuracy as a function of the number of diffusion steps on CIFAR-100 and ImageNet32-100 under 30% GN. The Parallel strategy converges rapidly, while Nested and Alternating require more steps to saturate.
Dataset
Corruption
Clean-estimate guidance
Noisy-sample guidance
CIFAR-100
15% GN
70.7
70.0
30% GN
68.5
67.0
Gaussian Blur
64.8
64.0
IN32-100
15% GN
69.8
69.6
30% GN
65.5
64.6
Gaussian Blur
63.3
61.8
Table 3: Effect of guidance source in the Parallel strategy. We compared conditioning on the clean estimate ( x^0(t) , y^0(t+1) ) versus the noisy sample ( xt−1 , yt ). Classification accuracy (%) is reported.
Corruption
DDPM
DDIM
15% GN
73.5
70.7
30% GN
71.2
68.5
Gaussian Blur
69.7
64.8
Table 4: Evaluation of DDPM versus DDIM sampling algorithms for the Parallel strategy on CIFAR-100. Classification accuracy (%) is reported.
Corruption
Enhanced
Parallel (Ours)
15% GN
92.1
94.0
30% GN
86.5
92.4
Gaussian Blur
94.0
94.1
Table 5: Scaling to full-resolution ImageNet ( 224×224 , 10 classes). Classification accuracy (%) under different corruptions. We compare the Enhanced baseline with our Parallel strategy.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Qualitative signal enhancement examples on MNIST. Each row corresponds to a different test sample. From left to right: corrupted input (ground truth shown for reference), Enhanced baseline, Alternating, Parallel, and Nested strategies. The predicted class under each reconstruction is indicated in the figure. Our coupled strategies consistently recover digit structures that are visually clearer and semantically aligned with the true class, leading to correct predictions.
Inspired by recent developments in neural speech coding and diffusion-based language modeling, we tackle speech enhancement by modeling the conditional distribution of clean speech codes given noisy speech codes using absorbing discrete diffusion. The proposed approach, which we call ADDSE, leverages both the expressive latent space of neural audio codecs and the non-autoregressive sampling procedure of diffusion models. To efficiently model the hierarchical structure of residual vector quantization codes, we propose RQDiT, which combines techniques from RQ-Transformer and diffusion Transformers for non-autoregressive modeling. Results show competitive performance in terms of non-intrusive objective metrics on two datasets, especially at low signal-to-noise ratios and with few sampling steps. Code and audio examples are available online.
Philippe Gonzalez
Department of Health Technology, Technical University of Denmark
Diffusion models show potential for speech enhancement but lack linguistic guidance. We condition a diffusion-based model on wav2vec 2.0 features from noisy input, injected at the U-Net bottleneck via Feature-wise Linear Modulation (FiLM). Phonetic representations from wav2vec 2.0 features of degraded speech, anchor the reverse diffusion process. While a frozen wav2vec 2.0 encoder extracts features, a learned FiLM generator produces scale and shift parameters modulating the bottleneck with minimal overhead. Motivated by the optimal Bayesian causal estimator under a linear-Gaussian state-space model, FiLM coefficients are aggregated via exponential smoothing for temporal compression. Evaluation on VoiceBank-DEMAND and LibriMix shows competitive performance against the unconditioned baseline in PESQ, STOI, SI-SDR and DNSMOS. We consistently record an improvement of 0.4 on PESQ score, suggesting self-supervised representations effectively condition diffusion-based speech enhancement.
Shuubham Ojha, Carol Espy-Wilson
Institute for Systems Research, University of Maryland, College Park
Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation. To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process. This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: https://github.com/Reconsider80/DiSIINet.
Ying Chen, Jinyue Li, Qiankun Li
Shenzhen Research Institute, The Chinese University of Hong Kong · University of Science and Technology of China · Imperial Global Singapore, Imperial College London