Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
arXiv:2602.15405v1 Announce Type: new 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 l
arXiv:2602.15405v1 Announce Type: new 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 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.
Executive Summary
This article presents a novel framework for robust classification in noisy environments by integrating two interacting diffusion models for signal enhancement and classification. The proposed framework, termed joint enhancement and classification, leverages the semantic information in the classifier's output during denoising. This approach enables mutual guidance between the enhancing signal and the evolving class logits, leading to improved classification accuracy under diverse noise conditions. The framework is evaluated on image classification and automatic speech recognition tasks, demonstrating robust and flexible improvements over traditional sequential enhancement baselines. The proposed method has the potential to revolutionize the field of machine learning by providing a domain-agnostic framework for joint enhancement and classification.
Key Points
- ▸ The proposed framework integrates two interacting diffusion models for signal enhancement and classification.
- ▸ The framework enables mutual guidance between the enhancing signal and the evolving class logits.
- ▸ The approach surpasses traditional sequential enhancement baselines in image classification and automatic speech recognition tasks.
Merits
Strength in Robustness
The proposed framework demonstrates robust performance in noisy environments, outperforming traditional sequential enhancement baselines.
Flexibility in Application
The domain-agnostic framework can be applied to various machine learning tasks, including image classification and automatic speech recognition.
Efficiency in Implementation
The framework can be implemented without requiring any retraining or fine-tuning of the classifier, making it an efficient solution.
Demerits
Limitation in Complexity
The proposed framework involves the integration of two interacting diffusion models, which may increase computational complexity and require significant resources.
Potential for Overfitting
The framework may be prone to overfitting, particularly when dealing with small datasets or complex classification tasks.
Need for Further Evaluation
The framework should be evaluated on a wider range of tasks and datasets to ensure its robustness and generalizability.
Expert Commentary
The proposed framework represents a significant advancement in the field of machine learning, offering a novel approach to robust classification in noisy environments. The integration of two interacting diffusion models enables mutual guidance between the enhancing signal and the evolving class logits, leading to improved classification accuracy. However, the framework's complexity and potential for overfitting should be carefully evaluated. Furthermore, the framework's applicability to various machine learning tasks and its potential for policy-making implications underscore its significance.
Recommendations
- ✓ Future research should focus on further evaluating the framework's robustness and generalizability across various tasks and datasets.
- ✓ The framework should be applied to real-world applications to assess its practical implications and potential policy-making implications.
