Methods › General › Knowledge Distillation › OMGD
Online Multi-granularity Distillation
OMGD
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
OMGD, or Online Multi-Granularity Distillation is a framework for learning efficient GANs. The student generator is optimized in a discriminator-free and ground-truth-free setting. The scheme trains the teacher and student alternatively, promoting these two generators iteratively and progressively. The progressively optimized teacher generator helps to warm up the student and guide the optimization direction step by step.
Specifically, the student generator G_S only leverages the complementary teacher generators Gᵂ_T and Gᴰ_T for optimization and can be trained in the discriminator-free and ground-truth-free setting. This framework transfers different levels concepts from the intermediate layers and output layer to perform the knowledge distillation. The whole optimization is conducted on an online distillation scheme. Namely, Gᵂ_T, Gᴰ_T and G_S are optimized simultaneously and progressively.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Online Convex Optimization with Switching Cost and Delayed Gradients 18 Oct 2023 · 0 repositories · arXiv:2310.11880
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Online Multi-Granularity Distillation for GAN Compression 16 Aug 2021 · 1 repository · arXiv:2108.06908
Tasks archive 2025-07-28
1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
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| Translation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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