Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

NeurIPS 2018 Nitin BansalXiaohan ChenZhangyang Wang

This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways? We develop novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property... (read more)

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