Pytorch implementation of additive margin softmax as seen on original paper
https://arxiv.org/pdf/1801.05599.pdf
This is a loss function designed for embedding learning as it encourages intra-class distances to be small and inter-class distances to be larger, so the input to the layer should be a batchXembedding tensor and the output will be softmax logits which can then be passed to a NLL implementation, eg. input size 64X512 means 64 embeddings of size 512 each.
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This is a pytorch implementation of the am_softmax, this softmax layer includes the class assignment fully connected layer, as it is required for it to be normalized.
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