Comments (1)
Benjamin-Barda
commented on July 24, 2024
Even if I am not Dr. Chen I invite you to take a look at the original SIMSIAM paper more specifically in Section 3.
A prediction MLP head [15], denoted as h, transforms the output of one view and matches it to the other view. Denoting the two output vectors as p1 ,h(f(x1)) and z2 ,f(x2), we minimize their negative cosine similarity:
Keyword here is "negative". So it is the correct behaviour.
In other words the closest you get to -1 the better.
from simsiam.
Related Issues (20)
- loss question HOT 4
- Single GPU Training HOT 3
- Loss descent slowly HOT 1
- Checkpoints with 50 epochs HOT 1
- [Problem] problem occured when trained on custom dataset HOT 4
- difference between byol in table19 and simsiam? HOT 1
- larger batch size with linear scale does not work
- checkpoint for epoch 800
- Slow convergence with SGD linear evaluation HOT 1
- SyncBatchnorm usage in main_lincls.py HOT 2
- About the projection and prediction head dimension configs
- Loss collapse during training HOT 1
- About the shape of image[0] and image[1] HOT 1
- Pre-trained weights for SimCLR, MoCov2, BYOL, SwaV HOT 1
- self.encoder.fc in builder.py
- which vector is used in experiment (the vector atfer predictor or after projection)?
- Why are training and testing so slow HOT 2
- Could not find any class folder
- Loss increasing
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from simsiam.