A PyTorch-based repository for semi-supervised learning.

Table of Contents • Requirements • References • FAQ •

We support popular semi-supervised learning(SSL) algorithms as listed below:

• FixMatch

Supported datasets:

• CIFAR-10

• python 3.6+
• torch 1.71
• torchvision 0.5
• tensorboard
• numpy
• tqdm

• [1] Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko. Semi-supervised learning with ladder networks. InNeurIPS, pages 3546–3554, 2015.

• [2] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averagedconsistency targets improve semi-supervised deep learning results. InNeurIPS, pages 1195–1204, 2017.

• [3] Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning methodfor deep neural networks. InWorkshop on challenges in representation learning, ICML,volume 3, 2013.

• [4] Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. Virtual adversarial training:a regularization method for supervised and semi-supervised learning.IEEE TPAMI, 41(8):1979–1993, 2018.

• [5] David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and ColinRaffel. Mixmatch: A holistic approach to semi-supervised learning.NeurIPS, page 5050–5060,2019.

• [6] Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised data augmen-tation for consistency training.NeurIPS, 33, 2020.

• [7] David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang,and Colin Raffel. Remixmatch: Semi-supervised learning with distribution matching andaugmentation anchoring. InICLR, 2019.

• [8] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raf-fel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence.NeurIPS, 33, 2020.

• [9] Bowen Zhang, Yidong Wang, Wenxin Hou, Hao wu, Jindong Wang, Okumura Manabu, and Shinozaki Takahiro. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. NeurIPS, 2021.

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