  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 to choose component layers sequentially. 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
yields state-of-the-art results in comparison to the hand-crafted networks on
image classification, particularly, the best network generated by BlockQNN
achieves 2.35% top-1 error rate on CIFAR-10. (2) it offers tremendous reduction
of the search space in designing networks, spending only 3 days with 32 GPUs. A
faster version can yield a comparable result with only 1 GPU in 20 hours. (3)
it has strong generalizability in that the network built on CIFAR also performs
well on the larger-scale dataset. The best network achieves very competitive
accuracy of 82.0% top-1 and 96.0% top-5 on ImageNet.
