Hypernetwork-Based Augmentation

• URL: http://arxiv.org/abs/2006.06320v2
• Date: Thu, 7 Oct 2021 02:56:46 GMT
• Title: Hypernetwork-Based Augmentation
• Authors: Chih-Yang Chen and Che-Han Chang
• Abstract summary: We propose an efficient gradient-based search algorithm, called Hypernetwork-Based Augmentation (HBA) Our HBA uses a hypernetwork to approximate a population-based training algorithm. Our results show that HBA is competitive to the state-of-the-art methods in terms of both search speed and accuracy.
• Score: 1.6752182911522517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation is an effective technique to improve the generalization of deep neural networks. Recently, AutoAugment proposed a well-designed search space and a search algorithm that automatically finds augmentation policies in a data-driven manner. However, AutoAugment is computationally intensive. In this paper, we propose an efficient gradient-based search algorithm, called Hypernetwork-Based Augmentation (HBA), which simultaneously learns model parameters and augmentation hyperparameters in a single training. Our HBA uses a hypernetwork to approximate a population-based training algorithm, which enables us to tune augmentation hyperparameters by gradient descent. Besides, we introduce a weight sharing strategy that simplifies our hypernetwork architecture and speeds up our search algorithm. We conduct experiments on CIFAR-10, CIFAR-100, SVHN, and ImageNet. Our results show that HBA is competitive to the state-of-the-art methods in terms of both search speed and accuracy.

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