FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

• URL: http://arxiv.org/abs/2007.08505v1
• Date: Thu, 16 Jul 2020 17:55:31 GMT
• Title: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
• Authors: Chia-Wen Kuo and Chih-Yao Ma and Jia-Bin Huang and Zsolt Kira
• Abstract summary: We propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. These transformations also use information from both within-class and across-class representations that we extract through clustering. We demonstrate that our method is comparable to current state of art for smaller datasets while being able to scale up to larger datasets.
• Score: 64.32306537419498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, however, are limited to simple transformations such as traditional data augmentation or convex combinations of two images. In this paper, we propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. Importantly, these transformations also use information from both within-class and across-class prototypical representations that we extract through clustering. We use features already computed across iterations by storing them in a memory bank, obviating the need for significant extra computation. These transformations, combined with traditional image-based augmentation, are then used as part of the consistency-based regularization loss. We demonstrate that our method is comparable to current state of art for smaller datasets (CIFAR-10 and SVHN) while being able to scale up to larger datasets such as CIFAR-100 and mini-Imagenet where we achieve significant gains over the state of art (\textit{e.g.,} absolute 17.44\% gain on mini-ImageNet). We further test our method on DomainNet, demonstrating better robustness to out-of-domain unlabeled data, and perform rigorous ablations and analysis to validate the method.

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