用扩散模型提升图像分类在模糊数据下的准确率
Advancing Image Classification with Discrete Diffusion Classification Modeling
- 通过扩散过程建模输入图像对应的类别后验分布
- 在ImageNet上仅用几次扩散迭代就超越传统分类器
- 适合数据少或图像受损的复杂分类场景
图像分类是计算机视觉中的经典任务，但在输入图像被污染或训练数据有限等高不确定性条件下仍具挑战性。传统方法直接从图像预测类别标签，可能性能不佳。本文提出离散扩散分类建模（DiDiCM），利用基于扩散的过程建模条件于输入图像的类别后验分布，支持对类别概率或离散类别标签进行扩散预测，灵活权衡计算与内存开销。我们在多个场景下进行了全面实验，结果表明：在ImageNet数据集上，仅需少数扩散迭代即可实现比基线更高的分类准确率，且任务越困难，优势越明显。代码已开源。
原文摘要 · Abstract (English)
Image classification is a well-studied task in computer vision, and yet it remains challenging under high-uncertainty conditions, such as when input images are corrupted or training data are limited. Conventional classification approaches typically train models to directly predict class labels from input images, but this might lead to suboptimal performance in such scenarios. To address this issue, we propose Discrete Diffusion Classification Modeling (DiDiCM), a novel framework that leverages a diffusion-based procedure to model the posterior distribution of class labels conditioned on the input image. DiDiCM supports diffusion-based predictions either on class probabilities or on discrete class labels, providing flexibility in computation and memory trade-offs. We conduct a comprehensive empirical study demonstrating the superior performance of DiDiCM over standard classifiers, showing that a few diffusion iterations achieve higher classification accuracy on the ImageNet dataset compared to baselines, with accuracy gains increasing as the task becomes more challenging. We release our code at https://github.com/omerb01/didicm .
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