# EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations

arXiv: https://arxiv.org/abs/1911.09265 · HTML: https://mlsanity.com/paper/1911.09265/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples](https://mlsanity.com/paper/2104.13963/) (2021)
- [Contrastive Learning with Stronger Augmentations](https://mlsanity.com/paper/2104.07713/) (2021)
- [Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection](https://mlsanity.com/paper/2205.03346/) (2022)
- [Self-Supervised Learning for Semi-Supervised Temporal Action Proposal](https://mlsanity.com/paper/2104.03214/) (2021)
- [On the Importance of Asymmetry for Siamese Representation Learning](https://mlsanity.com/paper/2204.00613/) (2022)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)
- [CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation](https://mlsanity.com/paper/2109.15170/) (2021)
- [Robust Deep Semi-Supervised Learning: A Brief Introduction](https://mlsanity.com/paper/2202.05975/) (2022)
- [Leveraging Systematic Knowledge of 2D Transformations](https://mlsanity.com/paper/2206.00893/) (2022)
- [Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy](https://mlsanity.com/paper/2110.06630/) (2021)

## Abstract (arXiv)

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 urlhttps://github.com/maple-research-lab/EnAET.