  With the recent development of deep convolutional neural networks and
large-scale datasets, deep face recognition has made remarkable progress and
been widely used in various applications. However, unlike the existing public
face datasets, in many real-world scenarios of face recognition, the depth of
training dataset is shallow, which means only two face images are available for
each ID. With the non-uniform increase of samples, such issue is converted to a
more general case, a.k.a long-tail face learning, which suffers from data
imbalance and intra-class diversity dearth simultaneously. These adverse
conditions damage the training and result in the decline of model performance.
Based on the Semi-Siamese Training (SST), we introduce an advanced solution,
named Multi-Agent Semi-Siamese Training (MASST), to address these problems.
MASST includes a probe network and multiple gallery agents, the former aims to
encode the probe features, and the latter constitutes a stack of networks that
encode the prototypes (gallery features). For each training iteration, the
gallery network, which is sequentially rotated from the stack, and the probe
network form a pair of semi-siamese networks. We give the theoretical and
empirical analysis that, given the long-tail (or shallow) data and training
loss, MASST smooths the loss landscape and satisfies the Lipschitz continuity
with the help of multiple agents and the updating gallery queue. The proposed
method is out of extra-dependency, thus can be easily integrated with the
existing loss functions and network architectures. It is worth noting that,
although multiple gallery agents are employed for training, only the probe
network is needed for inference, without increasing the inference cost.
Extensive experiments and comparisons demonstrate the advantages of MASST for
long-tail and shallow face learning.

Computer Vision and Pattern Recognition