信号与logits的耦合扩散模型联合增强与分类
Joint Enhancement and Classification using Coupled Diffusion Models of Signals and Logits
AI总结：
本文提出一种集成信号和logits扩散模型的框架，通过相互指导提升分类鲁棒性，有效应对噪声环境下的分类挑战。
摘要：
在噪声环境中实现稳健分类仍是机器学习的基本挑战。传统方法通常将信号增强和分类视为独立的顺序阶段：首先增强信号，然后应用分类器。这种方法未能利用分类器输出中的语义信息进行去噪。本文提出一种通用、领域无关的框架，整合两个相互作用的扩散模型：一个处理输入信号，另一个处理分类器的输出logits，无需重新训练或微调分类器。这种耦合形式使两者相互指导，其中增强的信号细化类别估计，反之，演化的类别logits引导信号重建朝着流形的判别区域发展。我们引入了三种策略来有效建模输入和logit的联合分布。我们评估了所提出的联合增强方法用于图像分类和自动语音识别。所提出的框架超越了传统顺序增强基线，在多样的噪声条件下实现了稳健且灵活的分类准确率提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
