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Image classification accuracy on the ImageNet dataset has been a barometer for progress in computer vision over the last decade.
Model similarity mitigates test set overuse
H. Mania, J. Miller, L. Schmidt, M. Hardt, and B. Recht · 1905
Earlier work this paper cites.
Wordnet: A lexical database for english
G. A. Miller · 1995
Earlier work this paper cites.
L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, and A. v. d. Oord · 2006
Earlier work this paper cites.
R. Geirhos, K. Meding, and F. A. Wichmann · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
Earlier work this paper cites.
Multi-column deep neural networks for image classification
D. Ciregan, U. Meier, and J. Schmidhuber · 2012
Earlier work this paper cites.
Why do classifier accuracies show linear trends under distribution shift?
H. Mania and S. Sra · 2012
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 F.-F. Li · 2015
Earlier work this paper cites.
Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
G. Van Horn, S. Branson, R. Farrell, S. Haber, J. Barry, P. Ipeirotis, P. Perona, and S. Belongie · 2015
Earlier work this paper cites.
On the (im) possibility of fairness
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
Earlier work this paper cites.
H. S. Lee, A. A. Agarwal, and J. Kim · 2017
Earlier work this paper cites.
P. Stock and M. Cissé · 2017
Earlier work this paper cites.
Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
Earlier work this paper cites.
Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. Van Der Maaten · 2018
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song · 2019
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What do compressed deep neural networks forget?
S. Hooker, A. Courville, G. Clark, Y. Dauphin, and A. Frome · 2019
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2019
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Do imagenet classifiers generalize to imagenet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Lingvo: a modular and scalable framework for sequence-to-sequence modeling
No one representation to rule them all: Overlapping features of training methods, 2021
R. Gontijo-Lopes, Y. Dauphin, and E. D. Cubuk · 2021
Later among the works it cites.
Scaling up visual and vision-language representation learning with noisy text supervision
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig · 2021
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Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth
T. Nguyen, M. Raghu, and S. Kornblith · 2021
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Confident learning: Estimating uncertainty in dataset labels
C. Northcutt, L. Jiang, and I. Chuang · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
C. G. Northcutt, A. Athalye, and J. Mueller · 2021
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J. Shen, P. Nguyen, Y. Wu, Z. Chen, M. X. Chen, Y. Jia, A. Kannan, T. Sainath, Y. Cao, C.-C. Chiu, et al · 2019
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Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, Z. Lipton, and E. P. Xing · 2019
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Do we train on test data? purging cifar of near-duplicates
B. Barz and J. Denzler · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, and J. Gilmer · 2020
Cited alongside, same era.
Big transfer (bit): General visual representation learning
A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly, and N. Houlsby · 2020
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Evaluating machine accuracy on imagenet
V. Shankar, R. Roelofs, H. Mania, A. Fang, B. Recht, and L. Schmidt · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al · 2020
Cited alongside, same era.
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna · 2021
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Combined scaling for zero-shot transfer learning
H. Pham, Z. Dai, G. Ghiasi, H. Liu, A. W. Yu, M.-T. Luong, M. Tan, and Q. V. Le · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Gspmd: general and scalable parallelization for ml computation graphs
Y. Xu, H. Lee, D. Chen, B. Hechtman, Y. Huang, R. Joshi, M. Krikun, D. Lepikhin, A. Ly, M. Maggioni, et al · 2021
Later among the works it cites.
Re-labeling imagenet: from single to multi-labels, from global to localized labels
S. Yun, S. J. Oh, B. Heo, D. Han, J. Choe, and S. Chun · 2021
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Datasets have worldviews
D. Baker · 2022
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M. Wortsman, G. Ilharco, S. Y. Gadre, R. Roelofs, R. Gontijo-Lopes, A. S. Morcos, H. Namkoong, A. Farhadi, Y. Carmon, S. Kornblith, et al · 2022
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Coca: Contrastive captioners are image-text foundation models
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