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High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
Cross-lingual language model pretraining
Lample, G. and Conneau, A · 1901
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Self-supervised visual feature learning with deep neural networks: A survey
Jing, L. and Tian, Y · 1902
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 1903
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Meta-sim: Learning to generate synthetic datasets
Kar, A., Prakash, A., Liu, M., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., and Fidler, S · 1904
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Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 1906
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Mandlekar, A., Booher, J., Spero, M., Tung, A., Gupta, A., Zhu, Y., Garg, A., Savarese, S., and Li, F · 1911
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 1912
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Semantic object classes in video: A high-definition ground truth database
Brostow, G. J., Fauqueur, J., and Cipolla, R · 2008
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Meta-sim2: Unsupervised learning of scene structure for synthetic data generation
Devaranjan, J., Kar, A., and Fidler, S · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Kai Li, and Li Fei-Fei · 2009
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Understanding fairness of gender classification algorithms across gender-race groups
Krishnan, A., Almadan, A., and Rattani, A · 2009
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Fed-sim: Federated simulation for medical imaging
Li, D., Kar, A., Ravikumar, N., Frangi, A. F., and Fidler, S · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R · 2012
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Efficient estimation of word representations in vector space, 2013
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
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GloVe: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Lifelong learning for sentiment classification
Chen, Z., Ma, N., and Liu, B · 2015
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Microsoft coco: Common objects in context, 2015
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P · 2015
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Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
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Learning visual features from large weakly supervised data
Joulin, A., van der Maaten, L., Jabri, A., and Vasilache, N · 2016
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Learning representations for automatic colorization
Larsson, G., Maire, M., and Shakhnarovich, G · 2016
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Lifelong machine learning: a paradigm for continuous learning
Liu, B · 2016
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Scenenet RGB-D: 5m photorealistic images of synthetic indoor trajectories with ground truth
McCormac, J., Handa, A., Leutenegger, S., and Davison, A. J · 2016
Cited alongside, same era.
Playing for data: Ground truth from computer games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., and Torralba, A · 2016
Cited alongside, same era.
Planet - photo geolocation with convolutional neural networks
Weyand, T., Kostrikov, I., and Philbin, J · 2016
Cited alongside, same era.
Expert gate: Lifelong learning with a network of experts
Aljundi, R., Chakravarty, P., and Tuytelaars, T · 2017
Cited alongside, same era.
Reconciling modern machine learning practice and the bias-variance trade-off, 2019
Belkin, M., Hsu, D., Ma, S., and Mandal, S · 2019
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Neural architecture search: A survey, 2019
Elsken, T., Metzen, J. H., and Hutter, F · 2019
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Face recognition vendor test (frvt) part 3: Demographic effects, 2019
Grother, P., Ngan, M., and Hanaoka, K · 2019
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Rethinking imagenet pre-training
He, K., Girshick, R., and Dollar, P · 2019
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Learning deep representations by mutual information estimation and maximization, 2019
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Synthetic and natural noise both break neural machine translation
Belinkov, Y. and Bisk, Y · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
Cited alongside, same era.
Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L · 2017
Cited alongside, same era.
Deep learning is robust to massive label noise
Rolnick, D., Veit, A., Belongie, S., and Shavit, N · 2017
Cited alongside, same era.
An overview of gradient descent optimization algorithms, 2017
Ruder, S · 2017
Cited alongside, same era.
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Towards deep learning models resistant to adversarial attacks, 2019
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2019
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Understanding overfitting peaks in generalization error: Analytical risk curves for l 2 l_{2} and l 1 l_{1} penalized interpolation, 2019
Mitra, P. P · 2019
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Deep double descent: Where bigger models and more data hurt, 2019
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I · 2019
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Fast-scnn: Fast semantic segmentation network
Poudel, R. P. K., Liwicki, S., and Cipolla, R · 2019
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Transfusion: Understanding transfer learning for medical imaging
Raghu, M., Zhang, C., Kleinberg, J., and Bengio, S · 2019
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A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T · 2019
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Tencent ml-images: A large-scale multi-label image database for visual representation learning
Wu, B., Chen, W., Fan, Y., Zhang, Y., Hou, J., Liu, J., and Zhang, T · 2019
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Benign overfitting in linear regression, 2020
Bartlett, P. L., Long, P. M., Lugosi, G., and Tsigler, A · 2020
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Surprises in high-dimensional ridgeless least squares interpolation, 2020
Hastie, T., Montanari, A., Rosset, S., and Tibshirani, R. J · 2020
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Understanding generalization through visualizations
Huang, W. R., Emam, Z., Goldblum, M., Fowl, L., Terry, J. K., Huang, F., and Goldstein, T · 2020
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., and Ferrari, V · 2020
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Does label smoothing mitigate label noise?
Lukasik, M., Bhojanapalli, S., Menon, A., and Kumar, S · 2020
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Beit: Bert pre-training of image transformers, 2021
Bao, H., Dong, L., and Wei, F · 2021
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High-performance large-scale image recognition without normalization, 2021
Brock, A., De, S., Smith, S. L., and Simonyan, K · 2021
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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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A continual learning survey: Defying forgetting in classification tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2021
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Towards robustness against natural language word substitutions
Dong, X., Luu, A. T., Ji, R., and Liu, H · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Self-supervised learning: The dark matter of intelligence, 2021
LeCun, Y. and Misra, I · 2021
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Representative & fair synthetic data
Tiwald, P., Ebert, A., and Soukup, D. T · 2021
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Datasetgan: Efficient labeled data factory with minimal human effort
Zhang, Y., Ling, H., Gao, J., Yin, K., Lafleche, J., Barriuso, A., Torralba, A., and Fidler, S · 2021
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