Interpretability methods are critical components for examining and exploring
deep neural networks (DNN), as well as increasing our understanding of and
trust in them. Vision transformers (ViT), which can be trained to
state-of-the-art performance with a self-supervised learning (SSL) training
method, provide built-in attention maps (AM). While AMs can provide
high-quality semantic segmentation of input images, they do not account for any
signal coming from a downstream classifier. We introduce class-discriminative
attention maps (CDAM), a novel post-hoc explanation method that is highly
sensitive to the target class. Our method essentially scales attention scores
by how relevant the corresponding tokens are for the predictions of a
classifier head. Alternative to classifier outputs, CDAM can also explain a
user-defined concept by targeting similarity measures in the latent space of
the ViT. This allows for explanations of arbitrary concepts, defined by the
user through a few sample images. We investigate the operating characteristics
of CDAM in comparison with relevance propagation (RP) and token ablation maps
(TAM), an alternative to pixel occlusion methods. CDAM is highly
class-discriminative and semantically relevant, while providing implicit
regularization of relevance scores.
PyTorch implementation: \url{this https URL}
Web live demo: \url{this https URL}

深度神经网络的解释性方法是关键组成部分，用于检查和探索神经网络，增加我们对其的理解和信任。我们介绍了一种高度敏感于目标类别的后期解释方法，即类别判别性注意力图（CDAM），它通过缩放注意力分数来确定相应的令牌对分类器预测的相关性。与像素遮蔽方法相比，CDAM 具有高度的类别区分性和语义相关性，同时提供了隐式的相关性评分正则化。