BlockQNN: Efficient Block-Wise Neural Network Architecture Generation

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 43; no. 7; pp. 2314 - 2328
• Main Authors: Zhong, Zhao, Yang, Zichen, Deng, Boyang, Yan, Junjie, Wu, Wei, Shao, Jing, Liu, Cheng-Lin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Artificial neural networks; AutoML; Computer architecture; Computer vision; Construction; Convolutional neural network; Graphics processing units; Image classification; Indexes; Machine learning; Network architecture; neural architecture search; Neural networks; Q-learning; reinforcement learning; Strategy; Task analysis

Convolutional neural networks have gained a remarkable success in computer vision. However, most popular 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 b...