Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks

• URL: http://arxiv.org/abs/2103.06583v1
• Date: Thu, 11 Mar 2021 10:24:49 GMT
• Title: Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
• Authors: Theodoros Georgiou, Sebastian Schmitt, Thomas B\"ack, Wei Chen, Michael Lew
• Abstract summary: We propose a weight soft-regularization method based on the oblique manifold. We evaluate our method on the popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets.
• Score: 7.214681039134488
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.

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