AutoLoss-GMS: Searching Generalized Margin-based Softmax Loss Function for Person Re-identification

Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu

Abstract:

Person re-identification is a hot topic in computer vision,
and the loss function plays a vital role in improving the discrimination of the learned features. However, most existing models utilize the hand-crafted loss functions, which
are usually sub-optimal and challenging to be designed.
In this paper, we propose a novel method, AutoLoss-GMS,
to search the better loss function in the space of generalized margin-based softmax loss function for person reidentification automatically. Specifically, the generalized
margin-based softmax loss function is first decomposed into
two computational graphs and a constant. Then a general searching framework built upon the evolutionary algorithm is proposed to search for the loss function efficiently. The computational graph is constructed with a forward method, which can construct much richer loss function
forms than the backward method used in existing works.
In addition to the basic in-graph mutation operations, the
cross-graph mutation operation is designed to further improve the offspring’s diversity. The loss-rejection protocol, equivalence-check strategy and the predictor-based
promising-loss chooser are developed to improve the search
efficiency. Finally, experimental results demonstrate that
the searched loss functions can achieve state-of-the-art performance and be transferable across different models and
datasets in person re-identification.