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View Code? Open in Web Editor NEWSelf-Supervised Representation Learning by Rotation Feature Decoupling
License: MIT License
Self-Supervised Representation Learning by Rotation Feature Decoupling
License: MIT License
Hi, please tell me where is the prob.dat file?
Thanks.
Thanks for the code implementation, but I don't know how to implement the code of "generate the prob.dat file", could you share it? thanks.
Hi there,
Looking through the code it seems like transforms.RandomResizedCrop
is used when training with self-supervision but then it is disabled when training the linear classifier. I was just wondering if there was a reason for the change, and if it makes a big difference in the final performance?
Thanks!
Hi, congratulations on your work!
I was actually wondering if there is any way to run this code on a custom dataset?
Hi, what version of python is used for this project. I am having some issues loading modules like imp. Thanks
Hi,
Thanks for your work and for sharing the code, the results are very good on ImageNet and other big image datasets.
I was wondering if you ran experiments on smaller images such as the ones in the CIFARs. I tried runing them myself but I am not so sure about how to configure the NCE layer and the results I get are really poor.
Do you have any observations to share over this ?
Thanks
Hi,
Many thanks for sharing your work! I am playing with your method in CIFAR-10 and I was wondering if you could help me a bit on the behaviour that I should expect from the losses. Did you observe worse rotation accuracy/loss than when training the rotation method alone? Could this be normal considering that rotation accuracy is not necessarily a good measure and learning together with NPID is just helping on forming more discriminative features that don't help rotation accuracy?
Many thanks in advance!
Best,
Diego.
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