Comments (1)
I am not sure what you mean without the context. In general, loss curve decreases like what's shown in the paper. We have not tried on other dataset, but if the loss increases, it is generally not a good sign (unless the underlying data distribution changes).
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Related Issues (20)
- loss question HOT 4
- Single GPU Training HOT 3
- 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
- The value of loss function nn.CosineSimilarity is negative 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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