EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
arXiv:2012.15864
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.
arXiv commentAAAI 2021; 7 pages, 7 figures, 2 tables
References in corpus (16)
- Adam: A Method for Stochastic Optimization
- Conditional Generative Adversarial Nets
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Deep Residual Learning for Image Recognition
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- An Overview of Multi-Task Learning in Deep Neural Networks
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Semi-Supervised Learning with Deep Generative Models
- Improved Techniques for Training GANs
- Adversarially Learned Inference
- Semi-Supervised Learning with Generative Adversarial Networks
- NVAE: A Deep Hierarchical Variational Autoencoder
- Triple Generative Adversarial Nets
- Unsupervised Domain Adaptation through Self-Supervision
- Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
- Generate High Resolution Images With Generative Variational Autoencoder