This project is aimmed at reproducing the results in Learning with Bad Training Data via Iterative Trimmed Loss Minimization.
- Pytorch
- Numpy
- scikit-learn
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Linear Regression: Tests on sample sizes & Test on alphas has been finished.
- Results of test on sample sizes fit the original data pretty well (Figure provided).
- Results of test on alphas are not so correct (Figure provided).
- Test on "large noise" has not been tested as the definition of "large" noise need further investigation.
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WRN: There are some issues with training.
- With Dataloader as input, the accuracy is around 50%.
- With torch.Tensor as input (numpy data converte to torch.Tensor), the accuracy is only 16%.
Therefore, I did not produce any of the results from WRN.
Note:
- The sample sizes for Linear Regression do not exceed 10000 because of the possibility the process being killed on my computer.
- WRN is trained with 200 epoches.
- WRN source is from link
Detailed test code can be found in train_*.py