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Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning.
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
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Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Colorization as a Proxy Task for Visual Understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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A Survey on Deep Learning in Medical Image Analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, and Clara I. Sánchez · 2017
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Large Batch Training of Convolutional Networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge J. Belongie · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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1399 H&E-stained sentinel lymph node sections of breast cancer patients: The CAMELYON dataset
Geert Litjens, Peter Bandi, Babak Ehteshami Bejnordi, Oscar Geessink, Maschenka Balkenhol, Peter Bult, Altuna Halilovic, Meyke Hermsen, Rob van de Loo, Rob Vogels, Quirine F. Manson, Nikolas Stathonikos, Alexi Baidoshvili, Paul van Diest, Carla Wauters, Marcory van Dijk, and Jeroen van der Laak · 2018
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UMAP: Uniform Manifold Approximation and Projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger · 2018
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Rotation equivariant cnns for digital pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 2018
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A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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Learning representations by maximizing mutual information across views
Philip Bachman, R. Devon Hjelm, and William Buchwalter · 2019
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Skin data from the Visual Sweden project DROID
Karin Lindman, F. Rose Jerónimo, Martin Lindvall, and Caroline Bivik Stadler · 2019
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Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision
Jingyu Liu, Gangming Zhao, Yu Fei, Ming Zhang, Yizhou Wang, and Yizhou Yu · 2019
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Ming Y. Lu, Richard J. Chen, Jingwen Wang, Debora Dillon, and Faisal Mahmood · 2019
Cited alongside, same era.
Transfusion: Understanding Transfer Learning with Applications to Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
Cited alongside, same era.
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
Cited alongside, same era.
Albumentations: Fast and Flexible Image Augmentations
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin · 2020
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
On Mutual Information in Contrastive Learning for Visual Representations
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, and Noah Goodman · 2020
Later among the works it cites.
PGL: prior-guided local self-supervised learning for 3D medical image segmentation
Yutong Xie, Jianpeng Zhang, Zehui Liao, Yong Xia, and Chunhua Shen · 2020
Later among the works it cites.
Self-supervised learning of pixel-wise anatomical embeddings in radiological images
Ke Yan, Jinzheng Cai, Dakai Jin, Shun Miao, Adam P Harrison, Dazhou Guo, Youbao Tang, Jing Xiao, Jingjing Lu, and Le Lu · 2020
Later among the works it cites.
Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations
Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, and Yefeng Zheng · 2020
Later among the works it cites.
Big Self-Supervised Models Advance Medical Image Classification
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Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani, and Ender Konukoglu · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Cited alongside, same era.
Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka · 2020
Cited alongside, same era.
Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology
Olivier Dehaene, Axel Camara, Olivier Moindrot, Axel de Lavergne, and Pierre Courtiol · 2020
Cited alongside, same era.
A framework for contrastive self-supervised learning and designing a new approach
William Falcon and Kyunghyun Cho · 2020
Cited alongside, same era.
Shortcut Learning in Deep Neural Networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
Cited alongside, same era.
Self-Supervised Similarity Learning for Digital Pathology
Jacob Gildenblat and Eldad Klaiman · 2020
Cited alongside, same era.
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, and Mohammad Norouzi · 2021
Closest in time.
Large Image Datasets: A Pyrrhic Win for Computer Vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge Belongie · 2021
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Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations
Hongwei Li, Fei-Fei Xue, Krishna Chaitanya, Shengda Luo, Ivan Ezhov, Benedikt Wiestler, Jianguo Zhang, and Bjoern Menze · 2021
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SSLP: Spatial Guided Self-supervised Learning on Pathological Images
Jiajun Li, Tiancheng Lin, and Yi Xu · 2021
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Contrastive learning with hard negative samples
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2021
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Moco pretraining improves representation and transferability of chest x-ray models
Hari Sowrirajan, Jingbo Yang, Andrew Y Ng, and Pranav Rajpurkar · 2021
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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
Caroline Bivik Stadler, Martin Lindvall, Claes Lundström, Anna Bodén, Karin Lindman, Jeronimo Rose, Darren Treanor, Johan Blomma, Karin Stacke, Nicolas Pinchaud, Martin Hedlund, Filip Landgren, Mischa Woisetschläger, and Daniel Forsberg · 2021
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Viewmaker networks: Learning views for unsupervised representation learning
Alex Tamkin, Mike Wu, and Noah D. Goodman · 2021
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How Transferable Are Self-supervised Features in Medical Image Classification Tasks?
Tuan Truong, Sadegh Mohammadi, and Matthias Lenga · 2021
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Self-supervised learning from a multi-view perspective
Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2021
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Understanding the Behaviour of Contrastive Loss
Feng Wang and Huaping Liu · 2021
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Transpath: Transformer-based self-supervised learning for histopathological image classification
Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Junzhou Huang, Wei Yang, and Xiao Han · 2021
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Self-supervised Visual Representation Learning for Histopathological Images
Pengshuai Yang, Zhiwei Hong, Xiaoxu Yin, Chengzhan Zhu, and Rui Jiang · 2021
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Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, and James S Duncan · 2021
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Self supervised contrastive learning for digital histopathology
Ozan Ciga, Tony Xu, and Anne Louise Martel · 2022
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Self-supervised driven consistency training for annotation efficient histopathology image analysis
Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen, and Anne L. Martel · 2022
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