Comments (2)
The loss value varies in each iterations due to different initializations. One possible answer to your question is that A-Softmax loss (with large m, e.g. 4) is more difficult to optimize than center loss, so the loss surface may be much more "non-convex" than the center loss.
However, while the iteration goes, the variance of the final performance will be much lower. Thus it does not really matter whether each iteration remains the same as the previous training.
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Well, I do not agree your point as the initialization of the parameters have been fixed by using random seed and therefore the softmax loss value under no matter center or a-softwax of the first iteration should be same.
from sphereface.
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from sphereface.