Comments (3)
@TianQi-777, I think it can be.
The point in the SupCon paper is to see how single supervised contrastive loss
could work. Mixing them together makes the contribution of contrastive loss unclear. But in practice you can put them together and see.
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Ok, thanks for your prompt reply.
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@HobbitLong @TianQi-777 Hi, have you tried jointly training (SupCon Loss & CE Loss)? How is the result compared with the two-stage training? Thanks!
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Related Issues (20)
- The problem about the number of positives in code
- Why use two anchors? HOT 1
- Loss function is not convergent when batch-sizes smaller? HOT 4
- the mean and std of cifar dataset
- File "main_supcon.py", line 270, in main opt = parse_option() File "main_supcon.py", line 94, in parse_option assert opt.degrees is not None AssertionError
- Question about potentially incomplete usage of 'logits_mask' HOT 1
- Is this using resnet50x4 width? HOT 2
- file “main_linear.py”, line103, set_model(opt) ,if torch.cuda.is_available() is false, why model will not load_state_dict(state_dict) HOT 1
- Execution of code for ImageNet code (Moco trick) HOT 1
- loss computation: mean and not sum HOT 4
- CE vs CE + SupCon HOT 2
- Purpose of logits_mask? HOT 3
- NaN loss for no positive pair from SupCon
- Pre-trained model HOT 1
- What mean, std and other datapreprocessing transforms did you use for ImageNet? HOT 1
- Moco trick?
- Code for using the ImageNet pretrained model
- solved
- How to change backbone HOT 1
- about loss_in and loss_out in paper
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