HobbitLong / PyContrast

PyTorch implementation of Contrastive Learning methods
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How much gain can Jigsaw bring? #7

Closed lihao0374 closed 4 years ago

lihao0374 commented 4 years ago

Good job! I was wondering how much gain can Jigsaw bring ?

HobbitLong commented 4 years ago

As in the paper, it would help improve from 63.6% to 65.9% with 100 epochs training with a linear projection head.

No, those hyper-parameters are NOT tuned in the InfoMin paper. For the Color Jittering, it was tuned in SimCLR; for the crop_size of 0.2, it was used in MoCo, where they say they followed InstDis and I don't know whether they have tuned it. So in short, I just plot what is happening behind these parameter choices provided by previous papers.

I would close it for now, but please feel free to reopen it if you have further questions.

lihao0374 commented 4 years ago

As in the paper, it would help improve from 63.6% to 65.9% with 100 epochs training with linear projection head.

No, those hyper-parameters are NOT tuned in the InfoMin paper. For the Color Jittering, it was tuned in SimCLR; for the crop_size of 0.2, it was used in MoCo, where they say they followed InstDis, but I feel they might have tuned it. So in short, I just plot what is happening behind these parameter choices provided by previous papers.

I would close it for now, but please feel free to reopen it if you have further questions.

Thanks for your reply ~

HobbitLong commented 4 years ago

No worry at all :) It’s actually a reasonable question!

Jigsaw improves it from 63.6 to 65.9. But I haven’t ablated it in the full model

lihao0374 commented 4 years ago

No worry at all :) It’s actually a reasonable question!

Jigsaw improves it from 63.6 to 65.9. But I haven’t ablated it in the full model

Sorry to bother you again, could you tell me how to get the optimal parameters N=2 and M=10 in RA?

HobbitLong commented 4 years ago

Please refer to here: #3

lihao0374 commented 4 years ago

Please refer to here: #3

Many thanks !