Out-of-Distribution Detection with Uncertainty Enhanced Attention Maps
Yue Gao, Qinliang Su · 2021
Out-of-Distribution Detection (OOD) has attracted a lot of attention in the past decade, since its practical importance in safety-critical applications. Various methods have been proposed for solving this problem and most existing classifier-based methods have focused on the entire image when detecting the anomalies. However, in an image, there would be uncertain regions that confuse the model, these regions prevent us from detecting anomalies even classifying correctly. To address this issue, we propose a novel model namely Uncertainty Enhanced Attention for OOD Detection (UEAOD) that consider the uncertainty estimate of various regions and specify emphases placed on each region during OOD detection. Specifically, we propose to use a generative adversarial network (GAN) to generate surrogate OOD examples and estimate uncertainty of discriminator to obtain a gradient-based certainty attention map for feature. With the weighted feature, classifier will be focused on the certain regions so that we can improve OOD detection ability. We provide detailed empirical analysis of the method for OOD task and convincingly demonstrate the effectiveness of the proposed approach.