Fetching the paper…
Reading the bibliography…
We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of self-supervised learning.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven C.H. Hoi · 2005
Earlier work this paper cites.
LIBLINEAR: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning visual features from large weakly supervised data
Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache · 2016
Earlier work this paper cites.
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
Earlier work this paper cites.
Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Earlier work this paper cites.
Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
Earlier work this paper cites.
Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge J. Belongie · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Detectron
Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár, and Kaiming He · 2018
Cited alongside, same era.
Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, and Dinglong Huang · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
Later among the works it cites.
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
Later among the works it cites.
Verified uncertainty calibration
Ananya Kumar, Percy Liang, and Tengyu Ma · 2019
Later among the works it cites.
Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli · 2019
Later among the works it cites.
Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 2019
Later among the works it cites.
S4l: Self-supervised semi-supervised learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Cited alongside, same era.
Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
Cited alongside, same era.
Dimensionality-driven learning with noisy labels
Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, and James Bailey · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
Cited alongside, same era.
Iterative learning with open-set noisy labels
Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia · 2018
Cited alongside, same era.
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
Later among the works it cites.
Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition
Weihe Zhang, Yali Wang, and Yu Qiao · 2019
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Closest in time.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Closest in time.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2020
Closest in time.
Beyond synthetic noise: Deep learning on controlled noisy labels
Lu Jiang, Di Huang, Mason Liu, and Weilong Yang · 2020
Closest in time.
Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2020
Closest in time.
Large scale learning of general visual representations for transfer
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Closest in time.
Protonet: Learning from web data with memory
Yi Tu, Li Niu, Dawei Cheng, and Liqing Zhang · 2020
Closest in time.
Webly supervised image classification with self-contained confidence
Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, and Wayne Zhang · 2020
Closest in time.