S$^2$COPE: Self-Supervised Concept Discovery via Preference Learning

Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficult to understand what visual concepts they encode. Can self-supervision instead produce useful visual concepts that are directly understandable to people