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View Code? Open in Web Editor NEW[ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Home Page: https://openreview.net/pdf?id=mEdwVCRJuX4
License: MIT License
[ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Home Page: https://openreview.net/pdf?id=mEdwVCRJuX4
License: MIT License
Hi @kaidic, thank you for your contribution. Have you tried to combine the two methods of LDAM and HAR?
In this repo, "heteroskedastic_cifar.py" contains the code for adding noisy labels in cifar datasets but only has "step"-type imbalance. I tried to use "imbalance_cifar.py" from LDAM to produce experiments on long-tailed datasets. But the training seems abnormal: when I estimate the statistics through a pre-train step using "cifar_hetero_est.py" with IF-100 CIFAR10, the accuracy has achieved 81% during the pre-training. Could you explain why it happen?
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