Recall that the ArcFace loss discussed in the course is applicable where a softmax activation function...

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Recall that the ArcFace loss discussed in the course is applicable where a softmax activation function is used to find probabilities from 2 or more scores, ie. two or more neurons in the layer where softmax is applied. For binary classification, quite often a single probability is found by transforming the embedding vector into a single score and then applying the sigmoid function. If you also recall from the course, the special binary cross-entropy loss function is applied in this case. Assuming a unit-norm embedding vector e, a transformation matrix with a single unit-norm row vector w and zero bias, your task is to propose an Angular-Binary-Cross-Entropy that will impose a margin in angular space. Recall that the ArcFace loss discussed in the course is applicable where a softmax activation function is used to find probabilities from 2 or more scores, ie. two or more neurons in the layer where softmax is applied. For binary classification, quite often a single probability is found by transforming the embedding vector into a single score and then applying the sigmoid function. If you also recall from the course, the special binary cross-entropy loss function is applied in this case. Assuming a unit-norm embedding vector e, a transformation matrix with a single unit-norm row vector w and zero bias, your task is to propose an Angular-Binary-Cross-Entropy that will impose a margin in angular space.

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