  Training 1-bit deep convolutional neural networks (DCNNs) is one of the most
challenging problems in computer vision, because it is much easier to get
trapped into local minima than conventional DCNNs. The reason lies in that the
binarized kernels and activations of 1-bit DCNNs cause a significant accuracy
loss and training inefficiency. To address this problem, we propose Genetic
Binary Convolutional Networks (GBCNs) to optimize 1-bit DCNNs, by introducing a
new balanced Genetic Algorithm (BGA) to improve the representational ability in
an end-to-end framework. The BGA method is proposed to modify the binary
process of GBCNs to alleviate the local minima problem, which can significantly
improve the performance of 1-bit DCNNs. We develop a new BGA module that is
generic and flexible, and can be easily incorporated into existing DCNNs, such
asWideResNets and ResNets. Extensive experiments on the object classification
tasks (CIFAR, ImageNet) validate the effectiveness of the proposed method. To
highlight, our method shows strong generalization on the object recognition
task, i.e., face recognition, facial and person re-identification.
