Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN

CVPR 2020 Jingwen YeYixin JiXinchao WangXin GaoMingli Song

Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CNN) models, thus reduce the annotation cost. However, almost all existing methods demand massive training data, which may be unavailable due to privacy or transmission issues... (read more)

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