Orthogonal Convolutional Neural Networks

Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11505-11515


Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix representation of the convolutional kernel, instead of the common kernel orthogonality approach, which we show is only necessary but not sufficient for ensuring orthogonal convolutions. Our proposed orthogonal convolution requires no additional parameters and little computational overhead. It consistently outperforms the kernel orthogonality alternative on a wide range of tasks such as image classification and inpainting under supervised, semi-supervised and unsupervised settings. It learns more diverse and expressive features with better training stability, robustness, and generalization. Our code is publicly available.

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[pdf] [supp] [arXiv]
author = {Wang, Jiayun and Chen, Yubei and Chakraborty, Rudrasis and Yu, Stella X.},
title = {Orthogonal Convolutional Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}