EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs

• URL: http://arxiv.org/abs/2012.15864v2
• Date: Wed, 31 Mar 2021 17:13:04 GMT
• Title: EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
• Authors: Ayaan Haque
• Abstract summary: Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adrial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. This may require a model to converge to a separate data distribution for each task, which may reduce overall performance. We propose a novel GAN model namely External GAN (ECGAN) that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. However, this may require a model to converge to a separate data distribution for each task, which may reduce overall performance. While progress in semi-supervised learning has been made, less addressed are small-scale, fully-supervised tasks where even unlabeled data is unavailable and unattainable. We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes. Our method leverages a GAN to generate artificial data used to supplement supervised classification. More specifically, we attach an external classifier, hence the name EC-GAN, to the GAN's generator, as opposed to sharing an architecture with the discriminator. Our experiments demonstrate that EC-GAN's performance is comparable to the shared architecture method, far superior to the standard data augmentation and regularization-based approach, and effective on a small, realistic dataset.

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