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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn · David Berthelot · Nicholas Carlini · Zizhao Zhang · Han Zhang · Colin A Raffel · Ekin Dogus Cubuk · Alexey Kurakin · Chun-Liang Li

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1218

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model’s performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we propose FixMatch, an algorithm that is a significant simplification of existing SSL methods. 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. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch’s success. The code is available at https://github.com/google-research/fixmatch.

Author Information

Kihyuk Sohn (Google)
David Berthelot (Google Brain)
Nicholas Carlini (Google)
Zizhao Zhang (Google)
Han Zhang (Google)
Colin A Raffel (Google Brain)

My research focuses on machine learning techniques for sequential data. I am currently a resident at Google Brain. I recently completed a PhD in Electrical Engineering at Columbia University In LabROSA, supervised by Dan Ellis. My thesis focused on learning-based methods for comparing sequences. In 2010, I received a Master's in Music, Science and Technology from Stanford University's CCRMA, supervised by Julius O. Smith III. I did my undergrad at Oberlin College, where I majored in Mathematics.

Dogus Cubuk (Google Brain)
Alexey Kurakin (Google Brain)
Chun-Liang Li (Google)

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