This is an implementation of "Learning to Reweight Examples for Robust Deep Learning"
Most of the experiments are done on the imbalanced CIFAR datasets
This is an implementation of "Learning to Reweight Examples for Robust Deep Learning" (ICML 2018) in PyTorch
When I used resnet32 on imbalanced cifar100, after several iter, it raise the error:
grads = torch.autograd.grad(loss_meta, meta_net.params(), create_graph=True)
File "/usr/local/lib/python3.5/dist-packages/torch/autograd/init.py", line 145, in grad
inputs, allow_unused)
RuntimeError: CUDA out of memory. Tried to allocate 14.50 MiB (GPU 0; 10.92 GiB total capacity; 10.04 GiB already allocated; 3.50 MiB free; 127.91 MiB cached)
do you have any experience in solving the problem?
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