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PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 355 |
| 2026-08-02 | 355 |
| 2026-08-06 | 355 |
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# Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of [Learning to Reweight Examples for Robust Deep Learning](https://arxiv.org/abs/1803.09050). The paper addresses the problem of imbalanced and noisy datasets by learning a good weighting of examples using a small clean and balanced dataset. Please Let me know if there are any bugs in my code. Thank you! =) I implemented this on Python 3.6 using PyTorch 0.4.0. ### Dataset I only ran the experiments for the class imbalance problem. Following the paper, I created an imbalanced training dataset using class '4' and '9' of the MNIST dataset, where '9' is the dominant class. (code for creating the dataset is in data_loader.py) Note that the test set used to measure the performance is balanced. ### Some Results We can see that even at 0.995 proportion of the dominant class in the training data, the model still reaches 90+% accuracy on the balanced test data. <img src="proportion_experiments.png" width="400" /> ### Acknowledgements Adrien Ecoffet: https://github.com/AdrienLE
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matched fp:39d4a6367842b546, llm:description: 'PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning'
matched fp:39d4a6367842b546, llm:description: 'PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning'
matched fp:39d4a6367842b546, llm:description: 'PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning'