Comments (3)
Hi, @IgorSusmelj ,
Good catch! I also could not imagine it back then.
I think it's because the scale of features leaned by contrastive methods is very different. If you are not comfortable with large learning rate. Here are two ways to fix it: (1) add a non-parametric BN (e.g., nn.BatchNorm1d(in_channel, affine=False)
) right before the linear classification layer to normalize the feature, then you will have a normal learning rate. (2) use Adam
optimizer.
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Ok, thanks for the quick reply. That would indeed be a another way to deal with it.
However, I would further investigate the distribution of weights and features to figure out what exactly happened. This might even improve the stability of the model. I can look into it but won't have much time in the coming weeks.
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@IgorSusmelj , it would be great to see the distribution of features and weights and understand why! Perhaps It is a good research problem, which I also wonder a lot.
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