Comments (2)
Sure, I think a good thing to do here would be to evaluate it on a one-shot learning task, e.g. Omniglot. Train the embeddings on a subset on classes, than extract embeddings of the other classes treating them as examplar images and query images. Then check accuracies e.g. for kNN.
There are better approaches though for this kind of problems now (e.g. prototypical networks)
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Thanks.
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Related Issues (20)
- Citing Work
- how to classify
- Tripletloss finally returns to zero HOT 1
- #how could i reload the pre-trained
- #how could i reload the pre-trained parameters when I use siamese
- #how could i load the primary pre-trained params when I using the siamese module #
- utils.py:FunctionNegativeTripletSelector - 'anchor_positive' referenced before assignment when len(label_indices) < 2 HOT 6
- Implementation of Triplet loss on CASIA Web Face Dataset
- training
- References HOT 1
- BalancedBatchSampler: classes vs samples HOT 1
- How to use this siamese model for classification task?
- batch_szie
- batch_size
- datasets.py HOT 1
- ValueError when using BalancedBatchSampler
- I got this on cifar10 test set
- About generating all possible triplets using combinations() function HOT 1
- how tesT
- how should we use it to classify?
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