Reviews: Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

I think that even inliers do not have a unified learning target in AE-based methods is the key reason why AE-based methods fails. It will be nice to empirically verify this. For example, the authors can do a similar experiment as that in Figure 2. Anyway, I believe this paper has its contribution to the community. And surprisingly, the algorithms greatly improve the outlier detection performance compared to multiple AE-based methods. Questions: - The key point of this method is to create pseudo labels for the unlabeled data, which will augment the training data by default.