img_classification.md

Image Classification

2024

• Roll with the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning. [pdf] [code]
  • Yue Duan, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi. AAAI 2024

2023

• Towards Semi-supervised Learning with Non-random Missing Labels. [pdf] [code] [zhihu]

  • Yue Duan, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, Yinghuan Shi. ICCV 2023

• Semi-supervised learning made simple with self-supervised clustering. [pdf]

  • Enrico Fini, Pietro Astolfi, Karteek Alahari, Xavier Alameda-Pineda, Julien Mairal, Moin Nabi, Elisa Ricci. CVPR 2023

• FreeMatch: Self-adaptive Thresholding for Semi-Supervised Learning. [pdf] [code]

  • Yidong Wang et al. ICLR 2023

• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning. [pdf] [code]

  • Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, Marios Savvides. ICLR 2023

• Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data. [project page]

  • Yuhao Chen, Xin Tan, Borui Zhao, Zhaowei Chen, Renjie Song, Jiajun Liang, and Xuequan Lu. CVPR 2023

• PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training. [project page]

  • Qingjie Zeng, Yutong Xie, Zilin Lu, and Yong Xia. CVPR 2023

2022

• Semi-supervised Vision Transformers at Scale. [pdf]

  • Zhaowei Cai, Avinash Ravichandran, Paolo Favaro, Manchen Wang, Davide Modolo, Rahul Bhotika, Zhuowen Tu, Stefano Soatto. Neurips 2022

• Debiased Self-Training for Semi-Supervised Learning. [pdf]

  • Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long. Neurips 2022

• Robust Semi-Supervised Learning when Not All Classes have Labels. [pdf]

  • Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li. Neurips 2022

• USB: A Unified Semi-supervised Learning Benchmark for Classification. [pdf]

  • Yidong Wang et al. Neurips 2022

• An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning. [pdf]

  • Xiu-Shen Wei, H.-Y. Xu, Faen Zhang, Yuxin Peng, Wei Zhou. Neurips 2022

• On Non-Random Missing Labels In Semi-Supervised Learning [pdf]

  • Xinting Hu, Yulei Niu, Chunyan Miao, Xian-Sheng Hua, Hanwang Zhang, ICLR 2022

• Unsupervised Selective Labeling for More Effective Semi-Supervised Learning. [pdf] [code]

  • Xudong Wang, Long Lian, Stella X. Yu. ECCV 2022

• RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning. [pdf]

  • Yue Duan, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi. ECCV 2022

• MutexMatch: Semi-supervised Learning with Mutex-Based Consistency Regularization. [pdf]

  • Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, Yang Gao. IEEE Transactions on Neural Networks and Learning Systems

• Smoothed Adaptive Weighting for Imbalanced SSL: Improve Reliability Against Unknown Distribution Data. [pdf]

  • Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S Cheung, Chen-Nee Chuah. ICML 2022

• Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding. [pdf]

  • Lan-Zhe Guo, Yu-Feng Li. ICML 2022

• Continual Semi-Supervised Learning through Contrastive Interpolation Consistency. [pdf] [code]

  • Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara. Pattern Recognition Letters 2022

• SimMatch: Semi-supervised Learning with Similarity Matching. [pdf] [code]

  • Mingkai Zheng, Shan You, Lang Huang, Fei Wang, Chen Qian, Chang Xu. CVPR 2022

• DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced SSL. [pdf] [code]

  • Youngtaek Oh, Dong-Jin Kim, In So Kweon. CVPR 2022

• Debiased Learning from Naturally Imbalanced Pseudo-Labels. [pdf] [code]

  • Xudong Wang, Zhirong Wu, Long Lian, Stella X. Yu. CVPR 2022

• CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning. [pdf]

  • Yue Fan, Dengxin Dai, Bernt Schiele. CVPR 2022

• Class-Aware Contrastive Semi-Supervised Learning [pdf]

  • Fan Yang, Kai Wu, Shuyi Zhang, Guannan Jiang, Yong Liu, Feng Zheng, Wei Zhang, Chengjie Wang, Long Zeng. CVPR 2022

• ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification. [pdf] [code]

  • Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro. CVPR 2022

• Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples. [pdf]

  • Noo-ri Kim, Jee-Hyong Lee. CVPR 2022

• Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning. [pdf]

  • Hui Tang, Kui Jia. CVPR 2022

• Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data. [pdf]

  • Rundong He, Zhongyi Han, Xiankai Lu, Yilong Yin. CVPR 2022

• DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning. [pdf]

• Zhen Zhao, Luping Zhou, Yue Duan, Lei Wang, Lei Qi, Yinghuan Shi. CVPR 2022

• Contrastive Regularization for Semi-Supervised Learning. [pdf] [code]

  • Doyup Lee, Sungwoong Kim, Ildoo Kim, Yeongjae Cheon, Minsu Cho, Wook-Shin Han. CVPR 2022 Workshop

• AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. [pdf]

  • David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin. ICLR 2022

2021

• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. [pdf] [code]

  • Kuniaki Saito, Donghyun Kim, Kate Saenko. NeurIPS 2021

• FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. [pdf] [code]

  • Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki. NeurIPS 2021

• ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning. [pdf]

  • Hyuck Lee, Seungjae Shin, Heeyoung Kim. NeurIPS 2021

• DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples. [pdf]

  • Yi Xu, Jiandong Ding, Lu Zhang, Shuigeng Zhou. NeurIPS 2021

• STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification. [pdf]

  • Hasib Zunair, Yan Gobeil, Samuel Mercier, A. Ben Hamza. ACM MMSports 2021

• CoMatch: Semi-Supervised Learning With Contrastive Graph Regularization. [pdf]

  • Junnan Li, Caiming Xiong, Steven C.H. Hoi. ICCV 2021

• Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels. [pdf]

  • Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang. ICCV 2021

• Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments With Support Samples. [pdf] [code]

  • Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael Rabbat. ICCV 2021

• FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. [pdf] [code]

  • Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki. NeurIPS 2021

• Dash: Semi-Supervised Learning with Dynamic Thresholding. [pdf]

  • Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin. ICML 2021

• Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning. [pdf] [code]

  • Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, Alan Yuille. Preprint 2021

• Semi-supervised Long-tailed Recognition using Alternate Sampling. [pdf] [code]

  • Bo Liu, Haoxiang Li, Hao Kang, Nuno Vasconcelos, Gang Hua. Preprint 2021

• Semi-supervised Contrastive Learning with Similarity Co-calibration. [pdf]

  • Yuhang Zhang, Xiaopeng Zhang, Robert.C.Qiu, Jie Li, Haohang Xu, Qi Tian. Preprint 2021

• All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training. [pdf] [code]

  • Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari. CVPR 2021

• AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. [pdf]

  • Chengyue Gong, Dilin Wang, Qiang Liu. CVPR 2021

• Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification. [pdf]

  • Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi. CVPR 2021

• CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning. [pdf]

  • Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang. CVPR 2021

• Exponential Moving Average Normalization for Self-Supervised and Semi-Supervised Learning. [pdf]

  • Zhaowei Cai, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Zhuowen Tu, Stefano Soatto. CVPR 2021

• Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training. [pdf] [code]

  • Kai Sheng Tai, Peter Bailis, Gregory Valiant. ICML 2021

• Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments. [pdf] [code]

  • Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael Rabbat. Preprint 2021

• Poisoning the Unlabeled Dataset of Semi-Supervised Learning. [pdf]

  • Nicholas Carlini. Preprint 2021

• A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification. [pdf]

  • Jong-Chyi Su, Zezhou Cheng, Subhransu Maji. CVPR 2021

• SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification. [pdf] [code]

  • Zijian Hu, Zhengyu Yang, Xuefeng Hu, Ram Nevatia. CVPR 2021

• Adaptive Consistency Regularization for Semi-Supervised Transfer Learning. [pdf] [code]

  • Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, Dejing Dou. CVPR 2021

• Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization. [pdf]

  • Kien Duc Do, Truyen Tran, Svetha Venkatesh. AAAI 2021

• Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning. [pdf] [code]

  • Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez. AAAI 2021

• Explanation Consistency Training: Facilitating Consistency-Based SSL with Interpretability. [pdf]

  • Tao Han, Wei-Wei Tu, Yu-Feng Li. AAAI 2021

• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for SSL. [pdf]

  • Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah. ICLR 2021

• On Data-Augmentation and Consistency-Based Semi-Supervised Learning. [pdf]

  • Atin Ghosh, Alexandre H. Thiery. ICLR 2021

2020

• Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning. [pdf] [code]

  • Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin. NeurIPS 2020

• Rethinking the Value of Labels for Improving Class-Imbalanced Learning. [pdf] [code]

  • Yuzhe Yang, Zhi Xu. NeurIPS 2020

• One-bit Supervision for Image Classification. [pdf]

  • Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian. NeurIPS 2020

• Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning. [pdf]

  • Tao Han, Junyu Gao, Yuan Yuan, Qi Wang. NeurIPS 2020

• Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. [pdf]

  • Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing. NeurIPS 2020

• Big Self-Supervised Models are Strong Semi-Supervised Learners. [pdf] [code]

  • Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton. NeurIPS 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. NeurIPS 2020

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

  • Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. NeurIPS 2020

• Autoencoder-based Graph Construction for Semi-supervised Learning. [pdf]

  • Mingeun Kang, Kiwon Lee, Yong H. Lee, Changho Suh. ECCV 2020

• Time-Consistent Self-Supervision for Semi-Supervised Learning. [pdf]

  • Tianyi Zhou, Shengjie Wang, Jeff Bilmes. ICML 2020

• FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning. [pdf]

  • Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira. ECCV 2020

• Negative sampling in semi-supervised learning. [pdf]

  • John Chen, Vatsal Shah, Anastasios Kyrillidis. ICML 2020

• Milking CowMask for Semi-Supervised Image Classification. [pdf] [code]

  • Geoff French, Avital Oliver, Tim Salimans. Le. Preprint 2020

• Meta Pseudo Labels. [pdf]

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

• Self-training with Noisy Student improves ImageNet classification. [pdf] [code]

  • Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. CVPR 2020

• WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning. [pdf] [code]

  • Liheng Zhang, Guo-Jun Qi. CVPR 2020

• Generating Accurate Pseudo-labels in Semi-Supervised Learning and AvoidingOverconfident Predictions via Hermite Polynomial Activations. [pdf] [code]

  • Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N. Ravi, Vikas Singh. CVPR 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

• Adversarial Transformations for Semi-Supervised Learning. [pdf]

  • Teppei Suzuki, Ikuro Sato. AAAI 2020

• Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. [pdf] [code]

  • Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness IJCNN 2020

2019

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

  • David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel. NeurIPS 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 and David Lopez-Paz. IJCAI 2019

• A multi-scheme semi-supervised regression approach. [pdf]

  • Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas. Pattern Recognition Letters (2019)

• Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme. [pdf]

  • Nikos Fazakis, Vasileios G. Kanas, Christos Aridas, Stamatis Karlos, Sotiris Kotsiantis. MDPI Entropy 2019

• Learning to Impute: A General Framework for Semi-supervised Learning. [pdf] [code]

  • Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen. Preprint 2019

2018

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

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

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

  • Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii. TPAMI 2018

• Transductive Semi-Supervised Deep Learningusing Min-Max Features. [pdf]

  • Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng Ma, Xiaoyu Tao, Nanning Zheng. ECCV 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

• Tri-net for Semi-Supervised Deep Learning. [pdf]

  • Dong-Dong Chen, Wei Wang, Wei Gao, Zhi-Hua Zhou. IJICAI 2018

• An incremental self-trained ensemble algorithm. [pdf] [link]

  • Stamatis Karlos, Nikos Fazakis, Konstantinos Kaleris, Vasileios G. Kanas and Sotos Kotsiantis. EAIS 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. NeurIPS 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. NeurIPS 2017

• Self-Trained Stacking Model for Semi-Supervised Learning. [link]

  • Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos N. Sgarbas. IJAIT 2017

2016

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

• Learning with Pseudo-Ensembles. [pdf]

  • Philip Bachman, Ouais Alsharif, Doina Precup. NeurIPS 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