Network Search for Binary Networks

Neural networks with both high accuracy and small network size are urgently required for mobile phone applications. However, previous network search methods do not take network size into account. In this paper, we use the reinforcement learning method to search for networks offline, with both high accuracy and small network size. Gaussian policy is used to explore the number of convolutional channels in a finer manner. Parameter reward is included in our reward function to punish large networks. We also use binary networks to further reduce network size. Without skip connections or branches, the network generated by our method is competitive with other methods on Cifar-10. Our network is much smaller than networks generated by other network search methods. Besides, our accuracy is higher than original binary network reported in BinaryConnect and is competitive with other real-valued networks.