This paper proposes to learn high-performance deep ConvNets with sparse
neural connections, referred to as sparse ConvNets, for face recognition. The
sparse ConvNets are learned in an iterative way, each time one additional layer
is sparsified and the entire model is re-trained given the initial weights
learned in previous iterations. One important finding is that directly training
the sparse ConvNet from scratch failed to find good solutions for face
recognition, while using a previously learned denser model to properly
initialize a sparser model is critical to continue learning effective features
for face recognition. This paper also proposes a new neural correlation-based
weight selection criterion and empirically verifies its effectiveness in
selecting informative connections from previously learned models in each
iteration. When taking a moderately sparse structure (26%-76% of weights in the
dense model), the proposed sparse ConvNet model significantly improves the face
recognition performance of the previous state-of-the-art DeepID2+ models given
the same training data, while it keeps the performance of the baseline model
with only 12% of the original parameters.

本文提出了使用稀疏神经连接学习高性能的深度卷积神经网络（称为稀疏 ConvNets）进行人脸识别。通过迭代学习，每次稀疏化一层神经元，并使用先前迭代中学习的初始权重重新训练整个模型，作者发现将稀疏 ConvNet 直接从头开始训练对于人脸识别效果不佳。同时，作者提出了一种新的基于神经相关性的权重选择准则，并验证其有效性，用以在每次迭代中从先前学习的模型中选择有意义的连接。在适度稀疏的结构下（在稠密模型中的权重占比为 26%-76%），与 DeepID2+ 的性能相比，所提出的稀疏 ConvNet 从相同的训练数据中显著提高了人脸识别性能，与只有原始参数的基线模型性能相当。