Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching

Most self-supervised learning objectives defend against collapse but leave the target representation law unspecified. We formulate representation learning as Distribution Matching (DM), learning an augmentation-invariant encoder whose induced law matches an explicit geometric reference. The referenc