  In face recognition, designing margin-based (e.g., angular, additive,
additive angular margins) softmax loss functions plays an important role in
learning discriminative features. However, these hand-crafted heuristic methods
are sub-optimal because they require much effort to explore the large design
space. Recently, an AutoML for loss function search method AM-LFS has been
derived, which leverages reinforcement learning to search loss functions during
the training process. But its search space is complex and unstable that
hindering its superiority. In this paper, we first analyze that the key to
enhance the feature discrimination is actually \textbf{how to reduce the
softmax probability}. We then design a unified formulation for the current
margin-based softmax losses. Accordingly, we define a novel search space and
develop a reward-guided search method to automatically obtain the best
candidate. Experimental results on a variety of face recognition benchmarks
have demonstrated the effectiveness of our method over the state-of-the-art
alternatives.
