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Self-supervised representation learning approaches have recently surpassed their supervised learning counterparts on downstream tasks like object detection and image classification.
J.-M. Geusebroek, G. J. Burghouts, and A. W. Smeulders, “The amsterdam library of object images,”
2005
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results.” http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
2007
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in
2008
Earlier work this paper cites.
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations,” in
2009
Earlier work this paper cites.
I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, and A. Y. Ng, “Measuring invariances in deep networks,” in
2009
Earlier work this paper cites.
Y. Tang, R. Salakhutdinov, and G. Hinton, “Robust boltzmann machines for recognition and denoising,” in
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,”
2013
Earlier work this paper cites.
J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,”
2013
Earlier work this paper cites.
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with convolutional neural networks,” in
2014
Earlier work this paper cites.
Y. Xiang, R. Mottaghi, and S. Savarese, “Beyond pascal: A benchmark for 3d object detection in the wild,” in
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in
2015
Earlier work this paper cites.
X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in
2015
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,”
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
Cited alongside, same era.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in
2016
Cited alongside, same era.
C. Doersch, “Tutorial on variational autoencoders,”
2016
Cited alongside, same era.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Muller, A. Bibi, S. Giancola, S. Alsubaihi, and B. Ghanem, “Trackingnet: A large-scale dataset and benchmark for object tracking in the wild,” in
2018
Later among the works it cites.
2019
Later among the works it cites.
I. Misra and L. van der Maaten, “Self-supervised learning of pretext-invariant representations,”
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Tian, D. Krishnan, and P. Isola, “Contrastive multiview coding,”
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B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,”
2016
Cited alongside, same era.
C. Doersch and A. Zisserman, “Multi-task self-supervised visual learning,” in
2017
Cited alongside, same era.
X. Wang, K. He, and A. Gupta, “Transitive invariance for self-supervised visual representation learning,” in
2017
Cited alongside, same era.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan, “Learning features by watching objects move,” in
2017
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in
2017
Cited alongside, same era.
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Scene parsing through ade20k dataset,” in
2017
Cited alongside, same era.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,”
2018
Cited alongside, same era.
2019
Later among the works it cites.
X. Wang, A. Jabri, and A. A. Efros, “Learning correspondence from the cycle-consistency of time,” in
2019
Later among the works it cites.
P. Goyal, D. Mahajan, A. Gupta, and I. Misra, “Scaling and benchmarking self-supervised visual representation learning,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
B. Zhou, D. Bau, A. Oliva, and A. Torralba, “Comparing the interpretability of deep networks via network dissection,” in
2019
Later among the works it cites.
L. Huang, X. Zhao, and K. Huang, “Got-10k: A large high-diversity benchmark for generic object tracking in the wild,”
2019
Later among the works it cites.
2020
Closest in time.
X. Chen, H. Fan, R. Girshick, and K. He, “Improved baselines with momentum contrastive learning,”
2020
Closest in time.
2020
Closest in time.