2017

Practical Block-wise Neural Network Architecture Generation

Zhong, Zhao, Yan, Junjie, Wu, Wei et al.

Understand

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.

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