FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Autor:	Sohn, Kihyuk, Berthelot, David, Li, Chun-Liang, Zhang, Zizhao, Carlini, Nicholas, Cubuk, Ekin D., Kurakin, Alex, Zhang, Han, Raffel, Colin
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling. Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 -- just 4 labels per class. Since FixMatch bears many similarities to existing SSL methods that achieve worse performance, we carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch's success. We make our code available at https://github.com/google-research/fixmatch. Comment: Published at NeurIPS 2020 as a conference paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.07685 Zobrazit plný text záznamu View this record from Arxiv