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Learning image representations without human supervision is an important and active research field.
Three unfinished works on the optimal storage capacity of networks
Elizabeth Gardner and Bernard Derrida · 1989
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Dynamics of on-line gradient descent learning for multilayer neural networks
David Saad and Sara A Solla · 1996
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Visual categorization with bags of keypoints
Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray · 2004
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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Evolving modular fast-weight networks for control
Faustino Gomez and Jürgen Schmidhuber · 2005
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Model compression
Cristian Bucilǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Video google: Efficient visual search of videos
Josef Sivic and Andrew Zisserman · 2006
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Fisher kernels on visual vocabularies for image categorization
Florent Perronnin and Christopher Dance · 2007
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Evaluating bag-of-visual-words representations in scene classification
Jun Yang, Yu-Gang Jiang, Alexander G Hauptmann, and Chong-Wah Ngo · 2007
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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The Pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Chris Williams, John Winn, and Andrew Zisserman · 2010
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Aggregating local descriptors into a compact image representation
Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez · 2010
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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To aggregate or not to aggregate: Selective match kernels for image search
Giorgos Tolias, Yannis Avrithis, and Hervé Jégou · 2013
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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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Learning deep features for scene recognition using places database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei Efros · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Bayesian dark knowledge
Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling · 2015
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Distilling model knowledge
George Papamakarios · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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NetVLAD: CNN architecture for weakly supervised place recognition
Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, Lawrence Zitnick, and Martial Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei Efros · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, and Daan Wierstra · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei Efros · 2016
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Look, listen and learn
Relja Arandjelovic and Andrew Zisserman · 2017
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Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
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Superdepth: Self-supervised, super-resolved monocular depth estimation
Sudeep Pillai, Rareş Ambruş, and Adrien Gaidon · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Selfie: Self-supervised pretraining for image embedding
Trieu H Trinh, Minh-Thang Luong, and Quoc V Le · 2019
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz · 2019
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Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Cited alongside, same era.
ActionVLAD: Learning spatio-temporal aggregation for action classification
Rohit Girdhar, Deva Ramanan, Abhinav Gupta, Josef Sivic, and Bryan Russell · 2017
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
Cited alongside, same era.
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
Cited alongside, same era.
Unsupervised representation learning by sorting sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang · 2017
Cited alongside, same era.
Later among the works it cites.
Detectron2, 2019
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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CutMix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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AET vs. AED: Unsupervised representation learning by auto-encoding transformations rather than data
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo · 2019
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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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The missing data encoder: Cross-channel image completion with hide-and-seek adversarial network
Arnaud Dapogny, Matthieu Cord, and Patrick Pérez · 2020
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A framework for contrastive self-supervised learning and designing a new approach
William Falcon and Kyunghyun Cho · 2020
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Are all negatives created equal in contrastive instance discrimination?
Jonathan Frankle, David J Schwab, and Ari Morcos · 2020
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Learning representations by predicting bags of visual words
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2020
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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
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Data-efficient image recognition with contrastive predictive coding
Olivier Henaff · 2020
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QuEST: Quantized embedding space for transferring knowledge
Himalaya Jain, Spyros Gidaris, Nikos Komodakis, Patrick Pérez, and Matthieu Cord · 2020
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2020
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Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Unsupervised learning of dense visual representations
Pedro O Pinheiro, Amjad Almahairi, Ryan Y Benmalek, Florian Golemo, and Aaron Courville · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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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
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven Hoi · 2021
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