  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. These plug-and-play regularizations can be conveniently
incorporated into training almost any CNN without extra hassle. We then
benchmark their effects on state-of-the-art models: ResNet, WideResNet, and
ResNeXt, on several most popular computer vision datasets: CIFAR-10, CIFAR-100,
SVHN and ImageNet. We observe consistent performance gains after applying those
proposed regularizations, in terms of both the final accuracies achieved, and
faster and more stable convergences. We have made our codes and pre-trained
models publicly available:
https://github.com/nbansal90/Can-we-Gain-More-from-Orthogonality.
