Asymmetric Loss
Learn about asymmetric loss for both single-label and multi-label classification tasks.
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Asymmetric loss is different from standard loss functions. It operates differently on both positive and negative samples.
It’s extremely useful as a loss function for multi-label classification tasks. We can classify an image into a few positive labels and many negative labels, which will result in a positive-negative imbalance situation, making it harder to optimize.
Asymmetric loss mitigates this issue by dynamically down-weighting negative samples with high confidence. It’ll discard any potential mislabeled samples. It’s easy to implement without any increase in training time or complexity. The PyTorch Image ...