  The volume of convolutional neural network (CNN) models proposed for face
recognition has been continuously growing larger to better fit large amount of
training data. When training data are obtained from internet, the labels are
likely to be ambiguous and inaccurate. This paper presents a Light CNN
framework to learn a compact embedding on the large-scale face data with
massive noisy labels. First, we introduce a variation of maxout activation,
called Max-Feature-Map (MFM), into each convolutional layer of CNN. Different
from maxout activation that uses many feature maps to linearly approximate an
arbitrary convex activation function, MFM does so via a competitive
relationship. MFM can not only separate noisy and informative signals but also
play the role of feature selection between two feature maps. Second, three
networks are carefully designed to obtain better performance meanwhile reducing
the number of parameters and computational costs. Lastly, a semantic
bootstrapping method is proposed to make the prediction of the networks more
consistent with noisy labels. Experimental results show that the proposed
framework can utilize large-scale noisy data to learn a Light model that is
efficient in computational costs and storage spaces. The learned single network
with a 256-D representation achieves state-of-the-art results on various face
benchmarks without fine-tuning. The code is released on
https://github.com/AlfredXiangWu/LightCNN.
