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A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-supervised loss.
Best practices for convolutional neural networks applied to visual document analysis
Patrice Simard, David Steinkraus, and John Platt · 2003
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, J. Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Microsoft coco: Common objects in context
Tsung-Yi Lin, M. Maire, Serge J. Belongie, James Hays, P. Perona, D. Ramanan, Piotr Dollár, and C. L. Zitnick · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun · 2015
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Dataset augmentation in feature space, 2017
Terrance DeVries and Graham W. Taylor · 2017
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Colorization as a proxy task for visual understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Autoaugment: Learning augmentation policies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Mask r-cnn, 2018
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2018
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Boosting self-supervised learning via knowledge transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro, and Hamed Pirsiavash · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 2018
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mixup: Beyond empirical risk minimization, 2018
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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A theoretical analysis of contrastive unsupervised representation learning, 2019
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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Faster autoaugment: Learning augmentation strategies using backpropagation, 2019
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama · 2019
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Momentum contrast for unsupervised visual representation learning, 2019
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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An analysis of pre-training on object detection, 2019
Hengduo Li, Bharat Singh, Mahyar Najibi, Zuxuan Wu, and Larry S. Davis · 2019
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Fast autoaugment, 2019
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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Self-supervised learning of pretext-invariant representations, 2019
Ishan Misra and Laurens van der Maaten · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere, 2020
Tongzhou Wang and Phillip Isola · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Xlnet: Generalized autoregressive pretraining for language understanding, 2020
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le · 2020
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Rethinking pre-training and self-training, 2020
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, and Quoc V. Le · 2020
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Cutmix: Regularization strategy to train strong classifiers with localizable features, 2019
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Are all negatives created equal in contrastive instance discrimination?, 2020
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Exploring simple siamese representation learning, 2020
Xinlei Chen and Kaiming He · 2020
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Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka · 2020
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Learning representations by predicting bags of visual words, 2020
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2020
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Revisiting contrastive methods for unsupervised learning of visual representations, 2021
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
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Self-supervised pretraining of visual features in the wild, 2021
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, and Piotr Bojanowski · 2021
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Efficient visual pretraining with contrastive detection, 2021
Olivier J. Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, and João Carreira · 2021
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Prototypical contrastive learning of unsupervised representations
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Yonglong Tian, Olivier J. Henaff, and Aaron van den Oord · 2021
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Contrastive learning, multi-view redundancy, and linear models, 2021
Christopher Tosh, Akshay Krishnamurthy, and Daniel Hsu · 2021
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Dense contrastive learning for self-supervised visual pre-training, 2021
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2021
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What should not be contrastive in contrastive learning, 2021
Tete Xiao, Xiaolong Wang, Alexei A. Efros, and Trevor Darrell · 2021
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Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2021
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Instance localization for self-supervised detection pretraining
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Robust contrastive learning against noisy views
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Hyperbolic contrastive learning for visual representations beyond objects
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Crafting better contrastive views for siamese representation learning
Xiang Peng, Kai Wang, Zheng Hua Zhu, and Yang You · 2022
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