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Class imbalance and noisy labels are the norm rather than the exception in many large-scale classification datasets.
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Imagenet classification with deep convolutional neural networks
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Neural codes for image retrieval
A. Babenko, A. Slesarev, A. Chigorin, and V. S. Lempitsky · 2014
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and F. Li · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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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. S. Bernstein, A. C. Berg, and F. Li · 2015
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Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
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Deep image retrieval: Learning global representations for image search
A. Gordo, J. Almazán, J. Revaud, and D. Larlus · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Webvision database: Visual learning and understanding from web data
W. Li, L. Wang, W. Li, E. Agustsson, and L. Van Gool · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Making deep neural networks robust to label noise: A loss correction approach
G. Patrini, A. Rozza, A. K. Menon, R. Nock, and L. Qu · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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Large batch training of convolutional networks
Y. You, I. Gitman, and B. Ginsburg · 2017
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. W. Tsang, and M. Sugiyama · 2018
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton · 2020
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Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton · 2020
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Exploring simple siamese representation learning
X. Chen and K. He · 2020
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Bootstrap your own latent - A new approach to self-supervised learning
J. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. Á. Pires, Z. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko · 2020
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A survey of label-noise representation learning: Past, present and future
B. Han, Q. Yao, T. Liu, G. Niu, I. W. Tsang, J. T. Kwok, and M. Sugiyama · 2020
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L. Li, and L. Fei-Fei · 2018
Cited alongside, same era.
Cleannet: Transfer learning for scalable image classifier training with label noise
K. Lee, X. He, L. Zhang, and L. Yang · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
D. Mahajan, R. B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Unsupervised label noise modeling and loss correction
E. Arazo, D. Ortego, P. Albert, N. O’Connor, and K. McGuinness · 2019
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Self-labelling via simultaneous clustering and representation learning
Y. M. Asano, C. Rupprecht, and A. Vedaldi · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
K. Cao, C. Wei, A. Gaidon, N. Aréchiga, and T. Ma · 2019
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Class-balanced loss based on effective number of samples
Y. Cui, M. Jia, T. Lin, Y. Song, and S. J. Belongie · 2019
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. B. Girshick · 2020
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Decoupling representation and classifier for long-tailed recognition
B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
J. Li, R. Socher, and S. C. H. Hoi · 2020
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Early-learning regularization prevents memorization of noisy labels
S. Liu, J. Niles-Weed, N. Razavian, and C. Fernandez-Granda · 2020
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Long-tail learning via logit adjustment
A. K. Menon, S. Jayasumana, A. S. Rawat, H. Jain, A. Veit, and S. Kumar · 2020
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Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
S. Purushwalkam and A. Gupta · 2020
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X. Wu, E. Dyer, and B. Neyshabur · 2020
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Towards good practices for efficiently annotating large-scale image classification datasets
Y. Liao, A. Kar, and S. Fidler · 2021
Closest in time.
Learning from noisy labels with complementary loss functions
D.-B. Wang, Y. Wen, L. Pan, and M.-L. Zhang · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny · 2021
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