AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations

Understand

The cosine-based softmax losses and their variants achieve great success in deep learning based face recognition.

• However, hyperparameter settings in these losses have significant influences on the optimization path as well as the final recognition performance.
• Manually tuning those hyperparameters heavily relies on user experience and requires many training tricks.
• In this paper, we investigate in depth the effects of two important hyperparameters of cosine-based softmax losses, the scale parameter and angular margin parameter, by analyzing how they modulate the predicted classification probability.