EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
This work addresses the high cost of labeled data for deep neural networks, offering a novel enhancement to existing semi-supervised and supervised learning approaches, though it is incremental as it builds on current methods like MixMatch.
The paper tackles the problem of limited labeled data in deep learning by proposing the EnAET framework, which integrates self-supervised representations as a regularization term to improve semi-supervised and supervised learning methods, achieving significant performance gains across datasets, including in challenging scenarios with only 10 images per class.
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many methods for semi-supervised learning have been proposed and achieved excellent performance. In this study, we propose a new EnAET framework to further improve existing semi-supervised methods with self-supervised information. To our best knowledge, all current semi-supervised methods improve performance with prediction consistency and confidence ideas. We are the first to explore the role of {\bf self-supervised} representations in {\bf semi-supervised} learning under a rich family of transformations. Consequently, our framework can integrate the self-supervised information as a regularization term to further improve {\it all} current semi-supervised methods. In the experiments, we use MixMatch, which is the current state-of-the-art method on semi-supervised learning, as a baseline to test the proposed EnAET framework. Across different datasets, we adopt the same hyper-parameters, which greatly improves the generalization ability of the EnAET framework. Experiment results on different datasets demonstrate that the proposed EnAET framework greatly improves the performance of current semi-supervised algorithms. Moreover, this framework can also improve {\bf supervised learning} by a large margin, including the extremely challenging scenarios with only 10 images per class. The code and experiment records are available in \url{https://github.com/maple-research-lab/EnAET}.