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FixMatch

swMATH ID: 41277
Software Authors: Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel
Description: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. 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
Homepage: https://arxiv.org/abs/2001.07685
Source Code: https://github.com/google-research/fixmatch
Dependencies: Python
Keywords: Python; Machine Learning; arXiv_cs.LG; Computer Vision; Pattern Recognition; arXiv_cs.CV; arXiv_stat.ML; FixMatch; Semi-Supervised Learning
Related Software: ReMixMatch; MixMatch; Python; RandAugment; SGDR; PyTorch; ImageNet; CIFAR; FlexMatch; S4L; Scikit; SemiBoost; LAMDA-SSL; AutoAugment; AugMix; Adam; TADAM; SpecAugment; mixup; LargeVis
Cited in: 1 Publication

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1 Publication describing the Software Year
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel
2020

Cited in 1 Serial

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