Methods › General › Knowledge Distillation › LFME
Learning From Multiple Experts
LFME
Introduced by Liuyu Xiang et al. in Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Learning From Multiple Experts is a self-paced knowledge distillation framework that aggregates the knowledge from multiple 'Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of adaptive learning schedules: Self-paced Expert Selection and Curriculum Instance Selection, so that the knowledge is adaptively transferred to the 'Student'. The self-paced expert selection automatically controls the impact of knowledge distillation from each expert, so that the learned student model will gradually acquire the knowledge from the experts, and finally exceed the expert. The curriculum instance selection, on the other hand, designs a curriculum for the unified model where the training samples are organized from easy to hard, so that the unified student model will receive a less challenging learning schedule, and gradually learns from easy to hard samples.
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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LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization 22 Oct 2024 · 1 repository · arXiv:2410.17020Syntology ran 4 of 7 samples · 3 unverified · 7 pointer-only (licence)
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Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification 6 Jan 2020 · 1 repository · arXiv:2001.01536
Tasks archive 2025-07-28
4 tasks 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 |
|---|---|
| Domain Generalization | 1 |
| General Classification | 1 |
| Knowledge Distillation | 1 |
| Long-tail Learning | 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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