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 .

翻译：图像分类是计算机视觉领域一个被广泛研究的任务，但在高不确定性条件下（例如输入图像受损或训练数据有限时）仍面临挑战。传统的分类方法通常训练模型直接从输入图像预测类别标签，但这可能导致在此类场景下性能欠佳。为解决该问题，我们提出离散扩散分类建模（DiDiCM），这是一种新颖的框架，利用基于扩散的过程来建模以输入图像为条件的类别标签后验分布。DiDiCM支持在类别概率或离散类别标签上进行基于扩散的预测，为计算与内存权衡提供了灵活性。我们通过全面的实证研究证明了DiDiCM相较于标准分类器的优越性能，结果表明在ImageNet数据集上，仅需少量扩散迭代即可获得比基线方法更高的分类准确率，且随着任务难度增加，准确率提升更为显著。我们的代码发布于https://github.com/omerb01/didicm。