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View Code? Open in Web Editor NEWCode for ICLR2021 paper "Robust and Generalizable Visual Representation Learning via Random Convolutions"
Code for ICLR2021 paper "Robust and Generalizable Visual Representation Learning via Random Convolutions"
Hello!
When trying to run the script, I found that the “synth” dataset doesn't seem to download automatically. And the link provided in the code comments is not working for me either. I had to run the code without this dataset for now, hope you can provide other ways to download the dataset.
Hi, Thanks for the interesting work.
You said in your paper that your Deep-All results on PACS are not directly comparable with Jigen due to different implementations. Could you please clarify what are the differences between your implementation and JiGen? You know your Deep-All result is almost 5% (on Avg.) lower than that of Carlucci's implementations. In addition, do you know what causes this difference? augmentation, model initialization, optimizer, learning rate decay strategy, or something else?
Thanks a lot.
Hi, thanks for sharing your code. For the function F.kl_div(), the first parameter is input and the second is target. I am confused why the target is not p_mixture on L401-L403?
Thanks.
Thanks for you excellent work. I wonder when will you add those scripts for PACS and imagenet ?
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