Comments (1)
The author says 'it enables embedding vectors of data points to be affected by each other through its gradients' in original paper. And in Equation 6, we may note that the gradient of certain x is built on different x in a batch. I think the author considers this can 'leverage data-to-proxy relationship'. Although it's farfetched...
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Related Issues (20)
- The hyperparameters of other methods HOT 1
- Hyperparameters of Proxy_NCA HOT 1
- bug while evaluation with cub200
- Reproduce the performance in CUB HOT 5
- CUDA out of memory HOT 3
- CUDA out of memory HOT 11
- [UPDATE to code/train.py ] criterion = losses.Proxy_NCA().cuda() HOT 1
- Using gt box or expanding gt box? HOT 3
- Nice topic
- N-Pair Loss reproducability HOT 12
- Inference Get Class Prediction HOT 1
- Evaluation on ReID datasets HOT 1
- Error when I use "adamw" as the optimizer (UnboundLocalError: local variable 'beta1' referenced before assignment) HOT 2
- model.model.embedding bug HOT 1
- [bug] proxyNCA loss also has parameters that needs to optimized HOT 3
- BalancedSampler is highly inefficient and way too slow for even dataset with mild size HOT 1
- Evaluate SOP using pretrained model, R@1 much lower from the number on README.md
- Unfair comparison, your method has 100x higher lr
- Regarding training complexity HOT 2
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