Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key problem, however, is these methods are designed for
classification and categorization tasks, and their extension to explaining
generative models, e.g. variational autoencoders (VAE) is not trivial. In this
work, we take a step towards bridging this crucial gap, proposing the first
technique to visually explain VAEs by means of gradient-based attention. We
present methods to generate visual attention from the learned latent space, and
also demonstrate such attention explanations serve more than just explaining
VAE predictions. We show how these attention maps can be used to localize
anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD
dataset. We also show how they can be infused into model training, helping
bootstrap the VAE into learning improved latent space disentanglement,
demonstrated on the Dsprites dataset.

本文提出了一种基于梯度的注意力机制技术，用于解释变分自编码器（VAE）及其预测，该技术通过生成学习到的潜在空间的视觉注意力，可用于图像中异常的定位，并能融入模型训练来帮助改善潜在空间分解，这在 MVTec-AD 和 Dsprites 数据集上表现出了最先进的性能。