Methods › General › Sample Re-Weighting › Fast Sample Re-Weighting
Fast Sample Re-Weighting
Introduced by Zizhao Zhang et al. in Learning Fast Sample Re-weighting Without Reward Data
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Fast Sample Re-Weighting, or FSR, is a sample re-weighting strategy to tackle problems such as dataset biases, noisy labels and imbalanced classes. It leverages a dictionary (essentially an extra buffer) to monitor the training history reflected by the model updates during meta optimization periodically, and utilises a valuation function to discover meaningful samples from training data as the proxy of reward data. The unbiased dictionary keeps being updated and provides reward signals to optimize sample weights. Additionally, instead of maintaining model states for both model and sample weight updates separately, feature sharing is enabled for saving the computation cost used for maintaining respective states.
Papers archive 2025-07-28
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Learning Fast Sample Re-weighting Without Reward Data 7 Sep 2021 · 1 repository · arXiv:2109.03216
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Meta-Learning | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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