1.we study how to leverage the learned representations for one-class classification.
2.We achieve strong performance on visual one-class classification benchmarks. such as .
3.While contrastive representations have achieved state-of-the-art performance on visual recognition tasks ,we argue that it could
be problematic for one-class classification.
- A pictorial example is in Figure 2c, where thanks to augmented distribution, the inlier distribution may become more compact