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Data augmentation (DA) encodes invariance and provides implicit regularization critical to a model's performance in image classification tasks.
Exploring bias in gan-based data augmentation for small samples
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What do compressed deep neural networks forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A. (2019) · 1911
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WordNet: An Electronic Lexical Database
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Affinity and diversity: Quantifying mechanisms of data augmentation
Gontijo-Lopes, R., Smullin, S. J., Cubuk, E. D., and Dyer, E. (2020) · 2002
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Understanding and mitigating the tradeoff between robustness and accuracy
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J., and Liang, P. (2020) · 2002
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Dada: Differentiable automatic data augmentation
Li, Y., Hu, G., Wang, Y., Hospedales, T., Robertson, N. M., and Yang, Y. (2020) · 2003
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Beyer, L., Hénaff, O. J., Kolesnikov, A., Zhai, X., and Oord, A. v. d. (2020) · 2006
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Automated flower classification over a large number of classes
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Natural language processing with Python: analyzing text with the natural language toolkit
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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., et al. (2020) · 2010
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Wemix: How to better utilize data augmentation
Xu, Y., Noy, A., Lin, M., Qian, Q., Li, H., and Jin, R. (2020) · 2010
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Tagging performance correlates with author age
Hovy, D. and Søgaard, A. (2015) · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., Perona, P., and Belongie, S. (2015) · 2015
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Demographic dialectal variation in social media: A case study of african-american english
Blodgett, S. L., Green, L., and O’Connor, B. (2016) · 2016
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Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
Hauberg, S., Freifeld, O., Larsen, A. B. L., Fisher, J., and Hansen, L. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016) · 2016
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
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Learning to compose domain-specific transformations for data augmentation
Ratner, A. J., Ehrenberg, H., Hussain, Z., Dunnmon, J., and Ré, C. (2017) · 2017
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Gender and dialect bias in youtube’s automatic captions
Tatman, R. (2017) · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2017) · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T. (2018) · 2018
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V. (2018) · 2018
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Further advantages of data augmentation on convolutional neural networks
Hernández-García, A. and König, P. (2018) · 2018
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Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Stock, P. and Cisse, M. (2018) · 2018
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Population based augmentation: Efficient learning of augmentation policy schedules
Ho, D., Liang, E., Chen, X., Stoica, I., and Abbeel, P. (2019) · 2019
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Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S. (2019) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Re-labeling imagenet: from single to multi-labels, from global to localized labels
Yun, S., Oh, S. J., Heo, B., Han, D., Choe, J., and Chun, S. (2021) · 2021
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Metaaugment: Sample-aware data augmentation policy learning
Zhou, F., Li, J., Xie, C., Chen, F., Hong, L., Sun, R., and Li, Z. (2021) · 2021
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Classifiers should do well even on their worst classes
Bitterwolf, J., Meinke, A., Boreiko, V., and Hein, M. (2022) · 2022
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Regularising for invariance to data augmentation improves supervised learning
Botev, A., Bauer, M., and De, S. (2022) · 2022
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Adaaug: Learning class-and instance-adaptive data augmentation policies
Cheung, T.-H. and Yeung, D.-Y. (2022) · 2022
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q. (2019) · 2019
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Adatransform: Adaptive data transformation
Tang, Z., Peng, X., Li, T., Zhu, Y., and Metaxas, D. N. (2019) · 2019
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Fixing the train-test resolution discrepancy
Touvron, H., Vedaldi, A., Douze, M., and Jégou, H. (2019) · 2019
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Learning invariances in neural networks from training data
Benton, G., Finzi, M., Izmailov, P., and Wilson, A. G. (2020) · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V. (2020) · 2020
Cited alongside, same era.
Faster autoaugment: Learning augmentation strategies using backpropagation
Hataya, R., Zdenek, J., Yoshizoe, K., and Nakayama, H. (2020) · 2020
Cited alongside, same era.
Fujii, S., Ishii, Y., Kozuka, K., Hirakawa, T., Yamashita, T., and Fujiyoshi, H. (2022) · 2022
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Geiping, J., Goldblum, M., Somepalli, G., Shwartz-Ziv, R., Goldstein, T., and Wilson, A. G. (2022) · 2022
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Imagenet-x: Understanding model mistakes with factor of variation annotations
Idrissi, B. Y., Bouchacourt, D., Balestriero, R., Evtimov, I., Hazirbas, C., Ballas, N., Vincent, P., Drozdzal, M., Lopez-Paz, D., and Ibrahim, M. (2022) · 2022
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On feature learning in the presence of spurious correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G. (2022) · 2022
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Deconstructing distributions: A pointwise framework of learning
Kaplun, G., Ghosh, N., Garg, S., Barak, B., and Nakkiran, P. (2022) · 2022
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On uncertainty, tempering, and data augmentation in bayesian classification
Kapoor, S., Maddox, W. J., Izmailov, P., and Wilson, A. G. (2022) · 2022
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FFCV: Accelerating training by removing data bottlenecks
Leclerc, G., Ilyas, A., Engstrom, L., Park, S. M., Salman, H., and Madry, A. (2022) · 2022
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Lin, C.-H., Kaushik, C., Dyer, E. L., and Muthukumar, V. (2022) · 2022
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Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022) · 2022
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Bugs in the data: How imagenet misrepresents biodiversity
Luccioni, A. S. and Rolnick, D. (2022) · 2022
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Learning instance-specific data augmentations
Miao, N., Mathieu, E., Dubois, Y., Rainforth, T., Teh, Y. W., Foster, A., and Kim, H. (2022) · 2022
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When does bias transfer in transfer learning?
Salman, H., Jain, S., Ilyas, A., Engstrom, L., Wong, E., and Madry, A. (2022) · 2022
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Modeldiff: A framework for comparing learning algorithms
Shah, H., Park, S. M., Ilyas, A., and Madry, A. (2022) · 2022
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Id and ood performance are sometimes inversely correlated on real-world datasets
Teney, D., Lin, Y., Oh, S. J., and Abbasnejad, E. (2022) · 2022
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When does dough become a bagel? analyzing the remaining mistakes on imagenet
Vasudevan, V., Caine, B., Gontijo-Lopes, R., Fridovich-Keil, S., and Roelofs, R. (2022) · 2022
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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C. (2022) · 2022
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Zheng, Y., Zhang, Z., Yan, S., and Zhang, M. (2022) · 2022
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Does progress on object recognition benchmarks improve real-world generalization?
Richards, M., Kirichenko, P., Bouchacourt, D., and Ibrahim, M. (2023) · 2023
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