Semi-Supervised Generative Learning via Latent Space Distribution Matching
We introduce Latent Space Distribution Matching (LSDM), a novel framework for semi-supervised generative modeling of conditional distributions. LSDM operates in two stages: (i) learning a low-dimensional latent space from both paired and unpaired data, and (ii) performing joint distribution matching in this space via the 1-Wasserstein distance, using only paired data. This two-step approach minimizes an upper bound on the 1-Wasserstein distance between joint distributions, reducing reliance on scarce paired samples while enabling fast one-step generation. Theoretically, we establish non-asymptotic error bounds and demonstrate a key benefit of unpaired data: enhanced geometric fidelity in ...
| Authors | Kwong Yu Chong, Long Feng |
|---|---|
| Primary category | stat.ML |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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