There should be a stop gradient in the simsiam model

[nikheelpandey]

Hello,

I was using your implementation of SimSiam for contrastive learning. I noticed that the model that you have created has a few problems:

1. The "stop_gradient" part of the network is absent from your implementation. This model is effectively training both the path.

Could you please clarify how and where you are taking care of it?

[hhhdw]

def D(p, z, version='simplified'): # negative cosine similarity if version == 'original': z = z.detach() # stop gradient p = F.normalize(p, dim=1) # l2-normalize z = F.normalize(z, dim=1) # l2-normalize return -(p*z).sum(dim=1).mean()

elif version == 'simplified':# same thing, much faster. Scroll down, speed test in __main__
    return - F.cosine_similarity(p, z.detach(), dim=-1).mean()
else:
    raise Exception

There is a 'detach' after 'z' when compute loss