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Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far apart.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2003
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2004
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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The Pascal visual object classes (VOC) challenge
M. Everingham, L. V. Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
SUN Database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
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3D object representations for fine-grained categorization
J. Krause, M. S. J. Dengand, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. B. Blaschko, and A. Vedaldi · 2013
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Large-scale fine-grained visual categorization of birds
T. Berg, J. Liu, S. W. Lee, M. L. Alexander, D. W. Jacobs, and P. N. Belhumeur · 2014
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Food-101 Mining discriminative components with random forests
L. Bossard, M. Guillaumin, and L. V. Gool · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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DeCAF: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Cliquecnn: Deep unsupervised exemplar learning
M. A. Bautista, A. Sanakoyeu, E. Tikhoncheva, and B. Ommer · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
Cited alongside, same era.
Unsupervised deep embedding for clustering analysis
J. Xie, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Satellite image-based localization via learned embeddings
D.-K. Kim and M. R. Walter · 2017
Cited alongside, same era.
Self-labelling via simultaneous clustering and representation learning
Y. M. Asano, C. Rupprecht, and A. Vedaldi · 2020
Closest in time.
Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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Debiased contrastive learning
C.-Y. Chuang, J. Robinson, L. Yen-Chen, A. Torralba, and S. Jegelka · 2020
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Randaugment: Practical data augmentation with no separate search
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
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Shortcut learning in deep neural networks
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
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Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
Cited alongside, same era.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Cited alongside, same era.
Large batch training of convolutional networks with layer-wise adaptive rate scaling
B. Ginsburg, I. Gitman, and Y. You · 2018
Cited alongside, same era.
Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
Cited alongside, same era.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko · 2020
Closest in time.
Data-efficient image recognition with contrastive predictive coding
O. J. Henaff, A. Srinivas, J. D. Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. van den Oord · 2020
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Hard negative mixing for contrastive learning
Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, and D. Larlus · 2020
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Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
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Prototypical contrastive learning of unsupervised representations
J. Li, P. Zhou, C. Xiong, R. Socher, and S. C. H. Hoi · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. van der Maaten · 2020
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Contrastive learning with hard negative samples
J. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
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Unsupervised representation learning by invariance propagation
F. Wang, H. Liu, D. Guo, and S. Fuchun · 2020
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Conditional negative sampling for contrastive learning of visual representations
M. Wu, M. Mosse, C. Zhuang, D. Yamins, and N. Goodman · 2021
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Contrastive attraction and contrastive repulsion for representation learning
H. Zheng, X. Chen, J. Yao, H. Yang, C. Li, Y. Zhang, H. Zhang, I. Tsang, J. Zhou, and M. Zhou · 2021
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