Unsupervised Feature Learning by Cross-Level Discrimination between Instances and Groups

Unsupervised feature learning has made great strides with invariant mapping and instance-level discrimination, as benchmarked by classification on common datasets. However, these datasets are curated to be distinctive and class-balanced, whereas naturally collected data could be highly correlated within the class (with repeats at the extreme) and long-tail distributed across classes. The natural grouping of instances conflicts with the fundamental assumption of instance-level discrimination. Contrastive feature learning is thus unstable without grouping, whereas grouping without contrastive feature learning is easily trapped into degeneracy. We propose to integrate grouping into instance-level discrimination, not by imposing group-level discrimination, but by imposing cross-level discrimination between instances and groups. Our key insight is that attraction and repulsion between instances work at different ranges. In order to discover the most discriminative feature that also respects natural grouping, we ask each instance to repel groups of instances that are far from it. By pushing against common groups, this cross-level repulsion actively binds similar instances together. To further avoid the clash between grouping and discrimination objectives, we also impose them on separate features derived from the common feature. Our extensive experimentation demonstrates not only significant gain on datasets with high correlation and long-tail distributions, but also leading performance on multiple self-supervision benchmarks including CIFAR-10, CIFAR-100 and ImageNet, bringing unsupervised feature learning closer to real data applications.