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[2602.15405] Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
Summary
This paper presents a novel framework for joint signal enhancement and classification using coupled diffusion models, improving accuracy in noisy environments without retraining classifiers.
Why It Matters
The integration of signal enhancement and classification represents a significant advancement in machine learning, particularly for applications in image classification and automatic speech recognition. By addressing noise effectively, this approach enhances the reliability of machine learning systems in real-world scenarios, where noise is prevalent.
Key Takeaways
- Proposes a domain-agnostic framework integrating diffusion models for signal enhancement and classification.
- Enables mutual guidance between signal enhancement and class estimation, improving robustness against noise.
- Outperforms traditional sequential enhancement methods in classification accuracy across various noise conditions.
Computer Science > Machine Learning arXiv:2602.15405 (cs) [Submitted on 17 Feb 2026] Title:Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits Authors:Gilad Nurko, Roi Benita, Yehoshua Dissen, Tomohiro Nakatani, Marc Delcroix, Shoko Araki, Joseph Keshet View a PDF of the paper titled Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits, by Gilad Nurko and 6 other authors View PDF HTML (experimental) Abstract: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 c...
