Abstract
Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a
block-wise network generation pipeline called BlockQNN
which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by
the learning agent which is trained sequentially to choose
component layers. We stack the block to construct the whole
auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive
results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error
rate on CIFAR-10 which beats all existing auto-generate
networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which
only spends 3 days with 32 GPUs, and (3) moreover, it has
strong generalizability that the network built on CIFAR also
performs well on a larger-scale ImageNet dataset.