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

Yuling Jiao; Wensen Ma; Defeng Sun; Hansheng Wang; Yang Wang

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arXiv · 2502.14424

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

Yuling Jiao·Wensen Ma·Defeng Sun·Hansheng Wang·Yang Wang

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Abstract

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 reference law specifies what the learned representation distribution should look like, whereas a separately chosen discrepancy determines how deviations from this target are measured; here we use Mallows distance. The DM framework reveals a directional inverse: generative learning maps a tractable reference to data, whereas representation learning maps data to a designed reference law. We connect the population objective to class-centre separation and classification error and prove a non-asymptotic neural-sieve guarantee. Simulations and image benchmarks show manifold rectification, fine-grained structure and transfer across label spaces.

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stat.ML cs.AI cs.LG stat.ME

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BibTeX RIS

Yuling Jiao, Wensen Ma, Defeng Sun, Hansheng Wang, Yang Wang. 2025-02-20. Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching. https://arxiv.org/abs/2502.14424

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