Awesome Semi-Supervised Learning

A curated list of awesome Semi-Supervised Learning resources. Inspired by awesome-deep-vision, awesome-deep-learning-papers, and awesome-self-supervised-learning

Background

An example of the influence of unlabeled data in semi-supervised learning. (Image source: Wikipedia)

What is Semi-Supervised Learning?

It is a special form of classification. Traditional classifiers use only labeled data (feature / label pairs) to train. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice.

How many semi-supervised learning methods are there?

Many. Some often-used methods include: EM with generative mixture models, self-training, consistency regularization, co-training, transductive support vector machines, and graph-based methods. And with the advent of deep learning, the majority of these methods were adapted and intergrated into existing deep learning frameworks to take advantage of unlabled data.

How do semi-supervised learning methods use unlabeled data?

Semi-supervised learning methods use unlabeled data to either modify or reprioritize hypotheses obtained from labeled data alone. Although not all methods are probabilistic, it is easier to look at methods that represent hypotheses by p(y|x), and unlabeled data by p(x). Generative models have common parameters for the joint distribution p(x,y). It is easy to see that p(x) influences p(y|x). Mixture models with EM is in this category, and to some extent self-training. Many other methods are discriminative, including transductive SVM, Gaussian processes, information regularization, graph-based and the majority of deep learning based methods. Original discriminative training cannot be used for semi-supervised learning, since p(y|x) is estimated ignoring p(x). To solve the problem, p(x) dependent terms are often brought into the objective function, which amounts to assuming p(y|x) and p(x) share parameters

(source: SSL Literature Survey.)

Contributing

If you find any errors, or you wish to add some papers, please feel free to contribute to this list by contacting me or by creating a pull request using the following Markdown format:

- Paper Name. 
  [[pdf]](link) 
  [[code]](link)
  - Author 1, Author 2, and Author 3. *Conference Year*

Table of contents

• Books
• Surveys & Overview
• Computer Vision
  • Image Classification
  • Semantic and Instance Segmentation
  • Object Detection
  • Other tasks
• NLP
• Generative Models
• Graph Based SSL
• Theory
• Reinforcement Learning, Meta-Learning & Robotics
• Regression
• Other
• Talks
• Thesis
• Blogs

Books

• Semi-Supervised Learning Book. [pdf]
  • Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. IEEE Transactions on Neural Networks 2009

Surveys & Overview

• Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. [pdf] [code]

  • Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow. NIPS 2018

• Semi-Supervised Learning Literature Survey. [pdf]

  • Xiaojin Zhu. 2008

Computer Vision

Note that for Image and Object segmentation tasks, we also include weakly-supervised learning methods, that uses weak labels (eg, image classes) for object and segmentation.

Image Classification

2020

• Meta Pseudo Labels. [pdf]

  • Hieu Pham, Qizhe Xie, Zihang Dai, Quoc V. Le. Preprint 2020

• A Simple Framework for Contrastive Learning of Visual Representations. [pdf] [code]

  • Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton. Preprint 2020

• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. [pdf] [code]

  • Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel. Preprint 2020

• ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. [pdf] [code]

  • David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel. ICLR 2020

• DivideMix: Learning with Noisy Labels as Semi-supervised Learning. [pdf] [code]

  • Junnan Li, Richard Socher, Steven C.H. Hoi. ICLR 2020

2019

• MixMatch: A Holistic Approach to Semi-Supervised Learning. [pdf] [code]

  • David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel. NIPS 2019

• Unsupervised Data Augmentation for Consistency Training. [pdf] [code]

  • Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Preprint 2019

• Dual Student: Breaking the Limits of the Teacher in Semi-Supervised Learning. [pdf] [code]

  • Zhanghan Ke, Daoye Wang, Qiong Yan, Jimmy Ren, Rynson W.H. Lau. ICCV 2019

• S4L: Self-Supervised Semi-Supervised Learning. [pdf] [code]

  • Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer. ICCV 2019

• Semi-Supervised Learning by Augmented Distribution Alignment. [pdf] [code]

  • Qin Wang, Wen Li, Luc Van Gool. ICCV 2019

• Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. [pdf]

  • Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu. CVPR 2019

• Label Propagation for Deep Semi-supervised Learning. [pdf]

  • Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum. CVPR 2019

• Joint Representative Selection and Feature Learning: A Semi-Supervised Approach. [pdf]

  • Suchen Wang, Jingjing Meng, Junsong Yuan, Yap-Peng Tan. CVPR 2019

• Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification. [pdf]

  • Si Wu, Jichang Li, Cheng Liu, Zhiwen Yu, Hau-San Wong. CVPR 2019

• There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. [pdf] [code]

  • Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson. ICLR 2019

• Semi-Supervised Learning by Label Gradient Alignment. [pdf]

  • Jacob Jackson, John Schulman. Preprint 2019

• Interpolation Consistency Training for Semi-Supervised Learning. [pdf] [code]

  • Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz. IJCAI 2019

2018

• Virtual adversarial training: a regularization method for supervised and semi-supervised learning. [pdf] [code]

  • Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii. IEEE Transactions on Pattern Analysis and Machine Intelligence 2018

• Deep Co-Training for Semi-Supervised Image Recognition. [pdf] [code]

  • Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo Wang, Alan Yuille. ECCV 2018

• HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning. [pdf]

  • Thomas Robert, Nicolas Thome, Matthieu Cord . ECCV 2018

• Transductive Centroid Projection for Semi-supervised Large-scale Recognition. [pdf]

  • Yu Liu, Guanglu Song, Jing Shao, Xiao Jin, Xiaogang Wang. ECCV 2018

• Semi-Supervised Deep Learning with Memory. [pdf]

  • Yanbei Chen, Xiatian Zhu, Shaogang Gong. ECCV 2018

• SaaS: Speed as a Supervisorfor Semi-supervised Learning. [pdf]

  • Safa Cicek, Alhussein Fawzi and Stefano Soatto. ECCV 2018

• ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification. [pdf]

  • Sima Behpour, Wei Xing, Brian D. Ziebart. AAAI 2018

• Adversarial Dropout for Supervised and Semi-Supervised Learning. [pdf]

  • Sungrae Park, JunKeon Park, Su-Jin Shin, Il-Chul Moon. AAAI 2018

2017

• Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks. [pdf]

  • Philip Haeusser, Alexander Mordvintsev, Daniel Cremers. CVPR 2017

• Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. [pdf] [code]

  • Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar. ICLR 2017

• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. [pdf] [code]

  • Antti Tarvainen, Harri Valpola. NIPS 2017

• Temporal Ensembling for Semi-Supervised Learning. [pdf] [code]

  • Samuli Laine, Timo Aila. ICLR 2017

• Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification. [pdf]

  • Meng Yang, Lin Chen. AAAI 2017

• Semi-Supervised Classifications via Elastic and Robust Embedding. [pdf]

  • Yun Liu, Yiming Guo, Hua Wang, Feiping Nie, Heng Huang. AAAI 2017

• Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours. [pdf]

  • Feiping Nie, Guohao Cai, Xuelong Li. AAAI 2017

• Recurrent Ladder Networks. [pdf]

  • Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao, Antti Rasmus, Rinu Boney, Harri Valpola. NIPS 2017

2016

• Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. [pdf]
  • Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen. NIPS 2016

2015

• Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning. [pdf]

  • Chun-Guang Li, Zhouchen Lin, Honggang Zhang, Jun Guo. ICCV 2015

• Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification. [pdf]

  • Liping Jing, Liu Yang, Jian Yu, Michael K. Ng . CVPR 2015

• Semi-Supervised Learning With Explicit Relationship Regularization. [pdf]

  • Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt. CVPR 2015

• Semi-supervised Learning with Ladder Networks. [pdf] [code]

  • Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, Tapani Raiko. NIPS 2015

• Training Deep Neural Networks on Noisy Labels with Bootstrapping. [pdf]

  • Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, Andrew Rabinovich. ICLR 2015

2014

• Semi-supervised Spectral Clustering for Image Set Classification. [pdf]
  • Arif Mahmood, Ajmal Mian, Robyn Owens. CVPR 2014

2013

• Ensemble Projection for Semi-supervised Image Classification. [pdf]

  • Dengxin Dai, Luc Van Gool. ICCV 2013

• Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification. [pdf]

  • Bo Wang, Zhuowen Tu, John K. Tsotsos. ICCV 2013

• Pseudo-Label : The Simple and Efficient Semi-Supervised LearningMethod for Deep Neural Networks. [pdf]

  • Dong-Hyun Lee. ICML Workshop 2013

Semantic and Instance Segmentation

2020

• Semi-Supervised Semantic Segmentation with Cross-Consistency Training. [pdf]

  • Yassine Ouali, Céline Hudelot, Myriam Tami. CVPR 2020

• Semi-Supervised Semantic Image Segmentation with Self-correcting Networks. [pdf]

  • Mostafa S. Ibrahim, Arash Vahdat, Mani Ranjbar, William G. Macready. CVPR 2020

2019

• Semi-Supervised Semantic Segmentation with High- and Low-level Consistency. [pdf] [code]

  • Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, Ming-Hsuan Yang. TPAMI 2019

• Semi-supervised semantic segmentation needs strong, high-dimensional perturbations. [pdf]

  • Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, Graham Finlayson. Preprint 2019

• CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing. [pdf] [code]

  • Kevin Duarte, Yogesh S. Rawat, Mubarak Shah. ICCV 2019

• Universal Semi-Supervised Semantic Segmentation. [pdf] [code]

  • Tarun Kalluri, Girish Varma, Manmohan Chandraker, C V Jawahar. ICCV 2019

• Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations. [pdf] [code]

  • Jiwoon Ahn, Sunghyun Cho, Suha Kwak. CVPR 2019

• FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference. [pdf]

  • Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon. CVPR 2019

2018

• Adversarial Learning for Semi-Supervised Semantic Segmentation. [pdf] [code]

  • Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, Ming-Hsuan Yang. BMVC 2018

• Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features. [pdf]

  • Xiang Wang, Shaodi You, Xi Li, Huimin Ma. CVPR 2018

• Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation. [pdf] [code]

  • Jiwoon Ahn, Suha Kwak. CVPR 2018

• Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach. [pdf]

  • Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, Shuicheng Yan. CVPR 2018

• Tell Me Where to Look: Guided Attention Inference Network. [pdf]

  • Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, Yun Fu. CVPR 2018

• Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation. [pdf]

  • Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, Thomas S. Huang. CVPR 2018

• Weakly- and Semi-Supervised Panoptic Segmentation. [pdf] [code]

  • Qizhu Li, Anurag Arnab, Philip H.S. Torr. ECCV 2018

• Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing. [pdf] [code]

  • Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, Jingdong Wang.. ECCV 2018

• Transferable Semi-Supervised Semantic Segmentation. [pdf]

  • Huaxin Xiao, Yunchao Wei, Yu Liu, Maojun Zhang, Jiashi Feng. AAAI 2018

2017

• Semi Supervised Semantic Segmentation Using Generative Adversarial Network. [pdf]

  • Nasim Souly, Concetto Spampinato, Mubarak Shah. ICCV 2017

• Simple Does It: Weakly Supervised Instance and Semantic Segmentation. [pdf] [code]

  • Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele. CVPR 2017

• Learning random-walk label propagation for weakly-supervised semantic segmentation. [pdf]

  • Paul Vernaza, Manmohan Chandraker. CVPR 2017

2015

• Semi-Supervised Normalized Cuts for Image Segmentation. [pdf]

  • Selene E. Chew, Nathan D. Cahill. ICCV 2015

• Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation. [pdf] [code]

  • George Papandreou, Liang-Chieh Chen, Kevin Murphy, Alan L. Yuille. ICCV 2015

• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation. [pdf]

  • Seunghoon Hong, Hyeonwoo Noh, Bohyung Han. NIPS 2015

• BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. [pdf]

  • Jifeng Dai, Kaiming He, Jian Sun. CVPR 2015

• SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation. [pdf]

  • Ting Liu, Miaomiao Zhang, Mehran Javanmardi, Nisha Ramesh, Tolga Tasdizen. ECCV 2015

2013

• Semi-supervised Learning for Large Scale Image Cosegmentation. [pdf]

  • Zhengxiang Wang, Rujie Liu. ICCV 2013

• Semi-supervised Learning for Large Scale Image Cosegmentation. [pdf]

  • Ke Zhang, Wei Zhang, Yingbin Zheng, Xiangyang Xue. AAAI 2013

Object Detection

2019

• Consistency-based Semi-supervised Learning forObject Detection. [pdf] [code]

  • Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak. ICCV 2019

• NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection. [pdf]

  • Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia. ICCV 2019

• Semi-Supervised Video Salient Object Detection Using Pseudo-Labels. [pdf]

  • Pengxiang Yan, Guanbin Li, Yuan Xie, Zhen Li, Chuan Wang, Tianshui Chen, Liang Lin. ICCV 2019

• Transferable Semi-Supervised 3D Object Detection From RGB-D Data. [pdf]

  • Yew Siang Tang, Gim Hee Lee. ICCV 2019

• Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation. [pdf]

  • Chunfeng Song, Yan Huang, Wanli Ouyang, Liang Wang. CVPR 2019

2018

• Adversarial Complementary Learning for Weakly Supervised Object Localization. [pdf]
  • Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, Thomas Huang. CVPR 2018

2017

• ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. [pdf]
  • Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal. NIPS 2017

2016

• Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer. [pdf]
  • Yuxing Tang, Josiah Wang, Boyang Gao, Emmanuel Dellandrea, Robert Gaizauskas, Liming Chen. CVPR 2016

2015

• Watch and Learn: Semi-Supervised Learning for Object Detectors From Video. [pdf]
  • Ishan Misra, Abhinav Shrivastava, Martial Hebert. CVPR 2015

2013

• Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video. [pdf]
  • Yang Yang, Guang Shu, Mubarak Shah. CVPR 2013

Other tasks

2020

• Deep Semi-Supervised Anomaly Detection. [pdf] [code]
  • Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft. ICLR 2020

2019

• Semi-Supervised Generative Adversarial Hashing for Image Retrieval. [pdf]

  • Guan'an Wang, Qinghao Hu, Jian Cheng, Zengguang Hou. ECCV 2018

• Learning to Self-Train for Semi-Supervised Few-Shot Classification. [pdf] [code]

  • Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele. NIPS 2019

• Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. [pdf] [code]

  • Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen. NIPS 2019

• Semi-Supervised Monocular 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer. [pdf]

  • Jingtan Piao, Chen Qian, Hongsheng Li. ICCV 2019

• SO-HandNet: Self-Organizing Network for 3D Hand Pose Estimation With Semi-Supervised Learning. [pdf]

  • Yujin Chen, Zhigang Tu, Liuhao Ge, Dejun Zhang, Ruizhi Chen, Junsong Yuan. ICCV 2019

• Semi-Supervised Pedestrian Instance Synthesis and Detection With Mutual Reinforcement. [pdf]

  • Si Wu, Sihao Lin, Wenhao Wu, Mohamed Azzam, Hau-San Wong. ICCV 2019

• Semi-Supervised Skin Detection by Network With Mutual Guidance. [pdf]

  • Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, Jue Wang. ICCV 2019

• MONET: Multiview Semi-Supervised Keypoint Detection via Epipolar Divergence. [pdf]

  • Yuan Yao, Yasamin Jafarian, Hyun Soo Park. ICCV 2019

• 3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training. [pdf]

  • Dario Pavllo, Christoph Feichtenhofer, David Grangier, Michael Auli. CVPR 2019

• Semi-Supervised Transfer Learning for Image Rain Removal. [pdf]

  • Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu. CVPR 2019

• KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing. [pdf]

  • Mengshi Qi, Yunhong Wang, Jie Qin, Annan Li. CVPR 2019

2018

• Improving Landmark Localization With Semi-Supervised Learning. [pdf]

  • Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz. CVPR 2018

• Semi-Supervised Bayesian Attribute Learning for Person Re-Identification. [pdf]

  • Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang. AAAI 2018

2017

• Semi-Supervised Deep Learning for Monocular Depth Map Prediction. [pdf]
  • Yevhen Kuznietsov, Jorg Stuckler, Bastian Leibe. CVPR 2017

2016

• SemiContour: A Semi-Supervised Learning Approach for Contour Detection. [pdf]

  • Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang. CVPR 2016

• Semi-Supervised Vocabulary-Informed Learning [pdf]

  • Yanwei Fu, Leonid Sigal. CVPR 2016

2015

• Adaptively Unified Semi-Supervised Dictionary Learning With Active Points. [pdf]

  • Xiaobo Wang, Xiaojie Guo, Stan Z. Li . ICCV 2015

• Semi-Supervised Zero-Shot Classification With Label Representation Learning. [pdf]

  • Xin Li, Yuhong Guo, Dale Schuurmans. ICCV 2015

2014

• Semi-Supervised Coupled Dictionary Learning for Person Re-identification. [pdf]

  • Xiao Liu, Mingli Song, Dacheng Tao, Xingchen Zhou, Chun Chen, Jiajun Bu. CVPR 2014

• A Convex Formulation for Semi-Supervised Multi-Label Feature Selection. [pdf]

  • Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang. AAAI 2014

2013

• Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model. [pdf]

  • Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang. ICCV 2013

• Semi-supervised Learning with Constraints for Person Identification in Multimedia Data. [pdf]

  • Martin Bauml, Makarand Tapaswi, Rainer Stiefelhagen. CVPR 2013

2010-2000

• Multimodal semi-supervised learning for image classification. [pdf]

• Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmid. CVPR 2010

• Semi-supervised Discriminant Analysis. [pdf]

  • Deng Cai, Xiaofei He, Jiawei Han. ICCV 2007

NLP

2019

• Semi-supervised Semantic Role Labeling Using the Latent Words Language Model. [pdf]

  • Koen Deschacht, Marie-Francine Moens. EMNLP 2019

• Semi-Supervised Semantic Role Labeling with Cross-View Training. [pdf]

  • Rui Cai, Mirella Lapata. EMNLP 2019

• Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings. [pdf]

  • Hwiyeol Jo, Ceyda Cinarel. EMNLP 2019

• Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model. [pdf]

  • Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua. EMNLP 2019

• A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching. [pdf]

  • Jihun Choi, Taeuk Kim, Sang-goo Lee. ACL 2019

• A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation. [pdf]

  • Jinxin Chang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Ting Yang, Ruifang Wang. ACL 2019

• No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects. [pdf]

  • Chiyu Zhang, Muhammad Abdul-Mageedl. ACL 2019

• Paraphrase Generation for Semi-Supervised Learning in NLU. [pdf]

  • Eunah Cho, He Xie, William M. Campbell. NAACL 2019

• Graph-Based Semi-Supervised Learning for Natural Language Understanding. [pdf]

  • Zimeng Qiu, Eunah Cho, Xiaochun Ma, William Campbell. EMNLP 2019

• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function. [pdf]

• Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov. AAAI 2019

2018

• Strong Baselines for Neural Semi-supervised Learning under Domain Shift. [pdf] [code]

  • Sebastian Ruder, Barbara Plank. ACL 2018

• Simple and Effective Semi-Supervised Question Answering. [pdf]

  • Bhuwan Dhingra, Danish Danish, Dheeraj Rajagopal. NAACL 2018

• Semi-Supervised Disfluency Detection. [pdf]

  • Feng Wang, Wei Chen, Zhen Yang, Qianqian Dong, Shuang Xu, Bo Xu. COLING 2018

• Variational Sequential Labelers for Semi-Supervised Learning. [pdf]

  • Mingda Chen, Qingming Tang, Karen Livescu, Kevin Gimpel. EMNLP 2018

• Towards Semi-Supervised Learning for Deep Semantic Role Labeling. [pdf]

  • Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime Carbonell. EMNLP 2018

• Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification. [pdf]

  • Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier. EMNLP 2018

• Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. [pdf]

  • Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, Xiaoli Li. EMNLP 2018

• Semi-Supervised Learning for Neural Keyphrase Generation. [pdf]

  • Hai Ye, Lu Wang. EMNLP 2018

• Semi-Supervised Sequence Modeling with Cross-View Training. [pdf]

  • Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc Le. ACL 2018

• Semi-Supervised Learning with Declaratively Specified Entropy Constraints. [pdf]

  • Haitian Sun, William W. Cohen, Lidong Bing. NIPS 2018

• Semi-Supervised Prediction-Constrained Topic Models. [pdf]

  • Michael Hughes, Gabriel Hope, Leah Weiner, Thomas McCoy, Roy Perlis, Erik Sudderth, Finale Doshi-Velez. AISTATS 2018

• SEE: Towards Semi-Supervised End-to-End Scene Text Recognition. [pdf]

  • Christian Bartz, Haojin Yang, Christoph Meinel. AAAI 2018

• Inferring Emotion from Conversational Voice Data: A Semi-Supervised Multi-Path Generative Neural Network Approach. [pdf]

  • Suping Zhou, Jia Jia, Qi Wang, Yufei Dong, Yufeng Yin, Kehua Leis. AAAI 2018

2017

• Semi-supervised Multitask Learning for Sequence Labeling. [pdf] [code]

  • Marek Rei. ACL 2017

• Semi-supervised Structured Prediction with Neural CRF Autoencoder. [pdf]

  • Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser. EMNLP 2017

• Semi-supervised sequence tagging with bidirectional language models. [pdf]

  • Matthew Peters, Waleed Ammar, Chandra Bhagavatula, Russell Power. ACL 2017

• Variational Autoencoder for Semi-Supervised Text Classification. [pdf]

  • Weidi Xu, Haoze Sun, Chao Deng, Ying Tan. AAAI 2017

• Semi-Supervised Multi-View Correlation Feature Learning with Application to Webpage Classification. [pdf]

  • Xiao-Yuan Jing, Fei Wu, Xiwei Dong, Shiguang Shan, Songcan Chen. AAAI 2017

• Adversarial Training Methods for Semi-Supervised Text Classification. [pdf] [code]

  • Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine. ICLR 2017

2016

• Semi-supervised Clustering for Short Text via Deep Representation Learning. [pdf]

  • Zhiguo Wang, Haitao Mi, Abraham Ittycheriah. CoNLL 2016

• Semi-supervised Question Retrieval with Gated Convolutions. [pdf]

  • Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Kateryna Tymoshenko, Alessandro Moschitti, Lluís Màrquez. NAACL 2016

• Semi-supervised Word Sense Disambiguation with Neural Models. [pdf]

  • Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, Eric Altendorf. COLING 2016

• Semi-Supervised Learning for Neural Machine Translation. [pdf]

  • Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu. ACL 2016

• A Semi-Supervised Learning Approach to Why-Question Answering. [pdf]

  • Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Ryu Iida, Masahiro Tanaka, Julien Kloetzer. AAAI 2016

• Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint. [pdf]

  • Li Zhao, Minlie Huang, Ziyu Yao, Rongwei Su, Yingying Jiang, Xiaoyan Zhu. AAAI 2016

• Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings. [pdf] [code]

  • Rie Johnson, Tong Zhang. ICML 2016

2015

• Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains. [pdf]

  • Kaveh Taghipour, Hwee Tou Ng. NACCL 2015

• Semi-supervised Sequence Learning. [pdf] [code]

  • Andrew M. Dai, Quoc V. Le. NIPS 2015

• Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. [pdf]

  • Rie Johnson, Tong Zhang. NIPS 2015

• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets. [pdf]

  • Jinpeng Wang, Gao Cong, Xin Wayne Zhao, Xiaoming Li. AAAI 2015

2014

• Semi-Supervised Matrix Completion for Cross-Lingual Text Classification. [pdf]
  • Min Xiao, Yuhong Guo. AAAI 2014

2013

• Effective Bilingual Constraints for Semi-Supervised Learning of Named Entity Recognizers. [pdf]
  • Mengqiu Wang, Wanxiang Che, Christopher D. Manning. AAAI 2013

2011

• Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. [pdf]
  • Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, Christopher D. Manning. EMNLP 2011

2010

• Cross Language Text Classification by Model Translation and Semi-Supervised Learning. [pdf]

  • Lei Shi, Rada Mihalcea, Mingjun Tian. EMNLP 2010

• Simple Semi-Supervised Training of Part-Of-Speech Taggers. [pdf]

  • Anders Søgaard. ACL 2010

• Word Representations: A Simple and General Method for Semi-Supervised Learning. [pdf]

  • Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio. ACL 2010

• A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web. [pdf]

  • Zornitsa Kozareva, Eduard Hovy. EMNLP 2010

2009

• A Simple Semi-supervised Algorithm For Named Entity Recognition. [pdf]
  • Wenhui Liao, Sriharsha Veeramachaneni. NACCL 2009

2008

• SemiBoost: Boosting for Semi-Supervised Learning. [pdf]

  • Pavan Kumar Mallapragada, Rong Jin, Anil K. Jain, Yi Liu. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008

• Simple Semi-supervised Dependency Parsing. [pdf]

  • Terry Koo, Xavier Carreras, Michael Collins. ACL 2008

2006

• Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets. [pdf]
  • Roi Reichart, Ari Rappoport. ACL 2007

2006

• Effective Self-Training for Parsing. [pdf]

  • David McClosky, Eugene Charniak, Mark Johnson. ACL 2006

• Reranking and Self-Training for Parser Adaptation. [pdf]

  • David McClosky, Eugene Charniak, Mark Johnson. ACL 2006

Generative Models

2020

• Semi-Supervised Generative Modeling for Controllable Speech Synthesis. [pdf]
  • Raza Habib, Soroosh Mariooryad, Matt Shannon, Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton, David Kao, Tom Bagby. ICLR 2019

2019

• MarginGAN: Adversarial Training in Semi-Supervised Learning. [pdf] [code]

  • Jinhao Dong, Tong Lin. NIPS 2019

• Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder. [pdf]

  • Caio Corro, Ivan Titov. ICLR 2019

• Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification. [pdf]

  • Si Wu, Guangchang Deng, Jichang Li, Rui Li, Zhiwen Yu, Hau-San Wong. CVPR 2019

2018

• Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model. [pdf] [code]
  • Baris Gecer, Binod Bhattarai, Josef Kittler, Tae-Kyun Kim. ECCV 2018

2017

• Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks. [pdf]

  • Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang. NIPS 2017

• Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. [pdf]

  • Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher. NIPS 2017

• Learning Disentangled Representations with Semi-Supervised Deep Generative Models. [pdf] [code]

  • N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, Philip H.S. Torr. NIPS 2017

• Good Semi-supervised Learning that Requires a Bad GAN. [pdf] [code]

  • Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov. NIPS 2017

• Infinite Variational Autoencoder for Semi-Supervised Learning. [pdf]

  • Ehsan Abbasnejad, Anthony Dick, Anton van den Hengel. CVPR 2017

2016

• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks. [pdf]

• Jost Tobias Springenberg. ICLR 2016

• Semi-Supervised Learning with Generative Adversarial Networks. [pdf]

  • Augustus Odena. ICML 2016

2014

• Semi-supervised Learning with Deep Generative Models. [pdf]
  • Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling. NIPS 2014

Graph Based SSL

2020

• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. [pdf]

  • Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu. ICLR 2020

• Graph Inference Learning for Semi-supervised Classification. [pdf]

  • Chunyan Xu, Zhen Cui, Xiaobin Hong, Tong Zhang, Jian Yang, Wei Liu. ICLR 2020

2019

• Improved Semi-Supervised Learning with Multiple Graphs. [pdf]

  • Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi, Partha Talukdar. AISTATS 2019

• Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. [pdf] [code]

  • Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar. AISTATS 2019

• Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. [pdf] [code]

  • Pedro Mercado, Francesco Tudisco, Matthias Hein. NIPS 2019

• A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. [pdf]

  • Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh. NIPS 2019

• Graph Agreement Models for Semi-Supervised Learning. [pdf] [code]

  • Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, Andrew Tomkins. NIPS 2019

• Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets. [pdf] [code]

  • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo. NIPS 2019

• A Flexible Generative Framework for Graph-based Semi-supervised Learning. [pdf] [code]

  • Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei. NIPS 2019

• Semi-Supervised Learning With Graph Learning-Convolutional Networks. [pdf]

  • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, Bin Luo. CVPR 2019

• Label Efficient Semi-Supervised Learning via Graph Filtering. [pdf]

  • Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan. CVPR 2019

• Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks. [pdf]

  • Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, Weixiong Zhang. AAAI 2019

• Matrix Completion for Graph-Based Deep Semi-Supervised Learning. [pdf]

  • Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi. AAAI 2019

• Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification. [pdf]

  • Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Ustebay. AAAI 2019

2018

• Semi-Supervised Learning via Compact Latent Space Clustering. [pdf]

  • Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori. ICML 2018

• Bayesian Semi-supervised Learning with Graph Gaussian Processes. [pdf]

  • Yin Cheng Ng, Nicolo Colombo, Ricardo Silva. NIPS 2018

• Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning. [pdf]

  • Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang. CVPR 2018

• Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. [pdf]

  • Y Qimai Li, Zhichao Han, Xiao-ming W. AAAI 2018

• Interpretable Graph-Based Semi-Supervised Learning via Flows. [pdf]

  • Raif M. Rustamov, James T. Klosowski. AAAI 2018

2017

• Semi-Supervised Classification with Graph Convolutional Networks. [pdf] [code]
  • Thomas N. Kipf, Max Welling. ICLR 2017

2016

• Large-Scale Graph-Based Semi-Supervised Learning via Tree Laplacian Solver. [pdf]

• Yan-Ming Zhang, Xu-Yao Zhang, Xiao-Tong Yuan, Cheng-Lin Liu. AAAI 2016

• Revisiting Semi-Supervised Learning with Graph Embeddings. [pdf] [code]

  • Zhilin Yang, William Cohen, Ruslan Salakhudinov. ICML 2016

2014

• Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. [pdf]

  • Yuan Fang, Kevin Chang, Hady Lauw. ICML 2014

• A Multigraph Representation for Improved Unsupervised/Semi-supervised Learning of Human Actions. [pdf]

  • Simon Jones, Ling Shao. CVPR 2014

2014

• Semi-supervised Eigenvectors for Locally-biased Learning. [pdf]
  • Toke Hansen, Michael W. Mahoney. NIPS 2012

2012

• Semi-supervised Regression via Parallel Field Regularization. [pdf]
  • Binbin Lin, Chiyuan Zhang, Xiaofei He. NIPS 2011

2011

• Unsupervised and semi-supervised learning via L1-norm graph. [pdf]

  • Feiping Nie, Hua Wang, Heng Huang, Chris Ding. ICCV 2011

• Semi-supervised Regression via Parallel Field Regularization. [pdf]

  • Binbin Lin, Chiyuan Zhang, Xiaofei He. NIPS 2011

2010

• Semi-Supervised Learning with Max-Margin Graph Cuts. [pdf]

  • Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang. AISTATS 2010

• Large Graph Construction for Scalable Semi-Supervised Learning. [pdf]

  • Wei Liu, Junfeng He, Shih-Fu Chang. ICML 2010

2009

• Graph construction and b-matching for semi-supervised learning. [pdf]
  • Tony Jebara, Jun Wang, Shih-Fu Chang. ICML 2009

2005

• Cluster Kernels for Semi-Supervised Learning. [pdf]
  • Olivier Chapelle, Jason Weston, Bernhard Scholkopf. NIPS 2005

2004

• Regularization and Semi-supervised Learning on Large Graphs. [pdf]
  • Mikhail Belkin, Irina Matveeva, Partha Niyogi. COLT 2004

Theory

2019

• The information-theoretic value of unlabeled data in semi-supervised learning. [pdf]

  • Alexander Golovnev, David Pal, Balazs Szorenyi. ICML 2019

• Analysis of Network Lasso for Semi-Supervised Regression. [pdf]

  • Alexander Jung, Natalia Vesselinova. AISTATS 2019

• Semi-supervised clustering for de-duplication. [pdf]

  • Shrinu Kushagra, Shai Ben-David, Ihab Ilyas . AISTATS 2019

2018

• Semi-Supervised Learning with Competitive Infection Models. [pdf]

  • Nir Rosenfeld, Amir Globerson. AISTATS 2018

• The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. [pdf]

  • Jesse H. Krijthe, Marco Loog. NIPS 2018

• The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. [pdf]

  • Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing. NIPS 2018

2017

• Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. [pdf]
  • Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama. ICML 2017

2016

• Semi-Supervised Learning with Adaptive Spectral Transform. [pdf]

  • Hanxiao Liu, Yiming Yang. AISTATS 2016

• Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation. [pdf]

  • Sujith Ravi, Qiming Diao. AISTATS 2016

2014

• Wasserstein Propagation for Semi-Supervised Learning. [pdf]

  • Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher. ICML 2014

• High Order Regularization for Semi-Supervised Learning of Structured Output Problems. [pdf]

  • Yujia Li, Rich Zemel. ICML 2014

2013

• Correlated random features for fast semi-supervised learning. [pdf]

  • Brian McWilliams, David Balduzzi, Joachim M. Buhmann. NIPS 2013

• Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. [pdf]

  • Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama. ICML 2013

• Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. [pdf]

  • Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama. ICML 2013

• Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion. [pdf]

  • Jinfeng Yi, Lijun Zhang, Rong Jin, Qi Qian, Anil Jain. ICML 2013

2012

• A Simple Algorithm for Semi-supervised Learning withImproved Generalization Error Bound. [pdf]

  • Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han. ICML 2012

• Deterministic Annealing for Semi-Supervised Structured Output Learning. [pdf]

  • Paramveer Dhillon, Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam. AISTATS 2012

2011

• Semi-supervised Learning by Higher Order Regularization. [pdf]

  • Xueyuan Zhou, Mikhail Belkin. AISTATS 2011

• Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. [pdf]

  • Xueyuan Zhou, Nathan Srebro. AISTATS 2011

2010

• Semi-Supervised Dimension Reduction for Multi-Label Classification. [pdf]

  • Buyue Qian, Ian Davidson. AAAI 2010

• Semi-Supervised Learning via Generalized Maximum Entropy. [pdf]

  • Ayse Erkan, Yasemin Altun. AISTATS 2010

• Semi-supervised learning by disagreement. [pdf]

  • Zhi-Hua Zhou, Ming Li. Knowledge and Information Systems 2010

2009

• Semi-supervised Learning by Sparse Representation. [pdf]
  • Shuicheng Yan and Huan Wang. SIAM 2009

2008

• Worst-case analysis of the sample complexity of semi-supervised learning. [pdf]
  • Shai Ben-David, Tyler Lu, David Pal. COLT 2008

2007

• Generalization error bounds in semi-supervised classification under the cluster assumption. [pdf]
  • Philippe Rigollet. JMLR 2007

2005

• Semi-supervised learning by entropy minimization. [pdf]

  • Yves Grandvalet, Yoshua Bengio. NIPS 2005

• A co-regularization approach to semi-supervised learning with multiple views. [pdf]

  • Vikas Sindhwani, Partha Niyogi, Mikhail Belkin. ICML 2005

• Tri-Training: Exploiting Unlabeled DataUsing Three Classifiers. [pdf]

  • Zhou Zhi-Hua and Li Ming. IEEE Transactions on knowledge and Data Engineering 2005

2003

• Semi-supervised learning using gaussian fields and harmonic functions. [pdf]

  • Xiaojin Zhu, Zoubin Ghahramani, John Lafferty. ICML 2003

• Semi-supervised learning of mixture models. [pdf]

  • Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo. ICML 2003

1998

• Combining labeled and unlabeled data with co-training. [pdf]
  • Tom Michael Mitchell, Tom Mitchell. COLT 1998

Reinforcement Learning, Meta-Learning & Robotics

2018

• Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery. [pdf] [code]
  • Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine. ICLR 2020

2018

• Meta-Learning for Semi-Supervised Few-Shot Classification. [pdf] [code]
  • Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel. ICLR 2018

2017

• Generalizing Skills with Semi-Supervised Reinforcement Learning. [pdf]
  • Takeru Miyato, Andrew M. Dai, Ian Goodfellow. ICLR 2017

Regression

2018

• Minimax-optimal semi-supervised regression on unknown manifolds. [pdf]

  • Amit Moscovich, Ariel Jaffe, Nadler Boaz . AISTATS 2017

• Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. [pdf] [code]

  • Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Ga ̈el Varoquaux. NIPS 2018

2017

• Learning Safe Prediction for Semi-Supervised Regression. [pdf]
  • Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou. AAAI 2017

2015

• Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data. [pdf]
  • Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Ga ̈el Varoquaux. NIPS 2015

Other

2018

• Semi-Supervised Learning on Data Streams via Temporal Label Propagation. [pdf]
  • Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra . ICML 2018

2017

• Kernelized Evolutionary Distance Metric Learning for Semi-Supervised Clustering. [pdf]
  • Wasin Kalintha, Satoshi Ono, Masayuki Numao, Ken-ichi Fukui. AAAI 2017

2016

• Robust Semi-Supervised Learning through Label Aggregation. [pdf]

  • Yan Yan, Zhongwen Xu, Ivor W. Tsang, Guodong Long, Yi Yang. AAAI 2016

• Semi-Supervised Dictionary Learning via Structural Sparse Preserving. [pdf]

  • Di Wang, Xiaoqin Zhang, Mingyu Fan, Xiuzi Ye. AAAI 2016

2013

• Efficient Semi-supervised and Active Learning of Disjunctions. [pdf]
  • Nina Balcan, Christopher Berlind, Steven Ehrlich, Yingyu Liang. ICML 2013

Talks

• Semi-Supervised Learning and Unsupervised Distribution Alignment. [youtube].
  • CS294-158-SP20 UC Berkeley.
• Semi-Supervised Learning and Unsupervised Distribution Alignment. [youtube].
  • Pydata, Andreas Merentitis, Carmine Paolino, Vaibhav Singh.
• Overview of Unsupervised & Semi-supervised learning. [youtube].
  • AISC, Shazia Akbar.
• Semi-Supervised Learning. [youtube] [slides].
  • CMU Machine Learning 10-701, Tom M. Mitchell .

Thesis

• Fundamental limitations of semi-supervised learnin. Tyler Tian Lu. [pdf].
• Semi-Supervised Learning with Graphs. Xiaojin Zhu. [pdf].
• Semi-Supervised Learning for Natural Language. Percy Liang. [pdf].

Blogs

• An overview of proxy-label approaches for semi-supervised learning. Sebastian Ruder. [link].