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Existing image augmentation methods consist of two categories: perturbation-based methods and generative methods.
The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Forbes, M.; and Choi, Y. 2019 · 1904
Earlier work this paper cites.
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
Fei-Fei, L.; Fergus, R.; and Perona, P. 2004 · 2004
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.-E.; and Zisserman, A. 2008 · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. 2009 · 2009
Earlier work this paper cites.
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. 2020 · 2010
Earlier work this paper cites.
Cats and dogs
Parkhi, O. M.; Vedaldi, A.; Zisserman, A.; and Jawahar, C. V. 2012 · 2012
Earlier work this paper cites.
Collecting a large-scale dataset of fine-grained cars
Krause, J.; Deng, J.; Stark, M.; and Fei-Fei, L. 2013 · 2013
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M.; Maji, S.; Kokkinos, I.; Mohamed, S.; and Vedaldi, A. 2014 · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Wide Residual Networks
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
Earlier work this paper cites.
Style transfer from non-parallel text by cross-alignment
Shen, T.; Lei, T.; Barzilay, R.; and Jaakkola, T. 2017 · 2017
Earlier work this paper cites.
Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2018 · 2018
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
Earlier work this paper cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. J. 2019 · 2019
Earlier work this paper cites.
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D.; Zoph, B.; Shlens, J.; and Le, Q. V. 2020 · 2020
Earlier work this paper cites.
Transformers: State-of-the-Art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Scao, T. L.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. M. 2020 · 2020
Cited alongside, same era.
Random erasing data augmentation
Zhong, Z.; Zheng, L.; Kang, G.; Li, S.; and Yang, Y. 2020 · 2020
Cited alongside, same era.
Image classification with deep learning in the presence of noisy labels: A survey
Algan, G.; and Ulusoy, I. 2021 · 2021
Cited alongside, same era.
Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Changpinyo, S.; Sharma, P.; Ding, N.; and Soricut, R. 2021 · 2021
Cited alongside, same era.
Unifying vision-and-language tasks via text generation
Cho, J.; Lei, J.; Tan, H.; and Bansal, M. 2021 · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Later among the works it cites.
LAION-5B: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; Schramowski, P.; Kundurthy, S.; Crowson, K.; Schmidt, L.; Kaczmarczyk, R.; and Jitsev, J. 2022 · 2022
Later among the works it cites.
Bridge-Tower: Building Bridges Between Encoders in Vision-Language Representation Learning
Xu, X.; Wu, C.; Rosenman, S.; Lal, V.; Che, W.; and Duan, N. 2022 · 2022
Later among the works it cites.
Image Data Augmentation for Deep Learning: A Survey
Yang, S.; Xiao, W.-T.; Zhang, M.; Guo, S.; Zhao, J.; and Furao, S. 2022 · 2022
Later among the works it cites.
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
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Jia, C.; Yang, Y.; Xia, Y.; Chen, Y.-T.; Parekh, Z.; Pham, H.; Le, Q. V.; Sung, Y.-H.; Li, Z.; and Duerig, T. 2021 · 2021
Cited alongside, same era.
On feature normalization and data augmentation
Li, B.; Wu, F.; Lim, S.-N.; Belongie, S. J.; and Weinberger, K. Q. 2021 · 2021
Cited alongside, same era.
Image segmentation using deep learning: A survey
Minaee, S.; Boykov, Y.; Porikli, F. M.; Plaza, A. J.; Kehtarnavaz, N.; and Terzopoulos, D. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; Krueger, G.; and Sutskever, I. 2021 · 2021
Cited alongside, same era.
Reproducible scaling laws for contrastive language-image learning
Cherti, M.; Beaumont, R.; Wightman, R.; Wortsman, M.; Ilharco, G.; Gordon, C.; Schuhmann, C.; Schmidt, L.; and Jitsev, J. 2022 · 2022
Cited alongside, same era.
Is synthetic data from generative models ready for image recognition?
He, R.; Sun, S.; Yu, X.; Xue, C.; Zhang, W.; Torr, P. H. S.; Bai, S.; and Qi, X. 2022 · 2022
Cited alongside, same era.
Data augmentation approaches in natural language processing: A survey
Li, B.; Hou, Y.; and Che, W. 2022 · 2022
Cited alongside, same era.
Ye, J.; Gao, J.; Li, Q.; Xu, H.; Feng, J.; Wu, Z.; Yu, T.; and Kong, L. 2022 · 2022
Later among the works it cites.
Expanding Small-Scale Datasets with Guided Imagination
Zhang, Y.; Zhou, D.; Hooi, B.; Wang, K.; and Feng, J. 2022 · 2022
Later among the works it cites.
Akrout, M.; Gyepesi, B.; Holló, P.; Poór, A. K.; Kincso, B.; Solis, S.; Cirone, K. D.; Kawahara, J.; Slade, D.; Abid, L.; Kov’acs, M.; and Fazekas, I. 2023 · 2023
Closest in time.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
Bakhtiarnia, A.; Zhang, Q.; and Iosifidis, A. 2023 · 2023
Closest in time.
InstructPix2Pix: Learning to Follow Image Editing Instructions
Brooks, T.; Holynski, A.; and Efros, A. A. 2023 · 2023
Closest in time.
A data augmentation perspective on diffusion models and retrieval
Burg, M. F.; Wenzel, F.; Zietlow, D.; Horn, M.; Makansi, O.; Locatello, F.; and Russell, C. 2023 · 2023
Closest in time.
Extracting Training Data from Diffusion Models
Carlini, N.; Hayes, J.; Nasr, M.; Jagielski, M.; Sehwag, V.; Tramèr, F.; Balle, B.; Ippolito, D.; and Wallace, E. 2023 · 2023
Closest in time.
Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
Dunlap, L.; Umino, A.; Zhang, H.; Yang, J.; Gonzalez, J. E.; and Darrell, T. 2023 · 2023
Closest in time.
Your Diffusion Model is Secretly a Zero-Shot Classifier
Li, A. C.; Prabhudesai, M.; Duggal, S.; Brown, E. L.; and Pathak, D. 2023 · 2023
Closest in time.
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
Shipard, J.; Wiliem, A.; Thanh, K. N.; Xiang, W.; and Fookes, C. 2023 · 2023
Closest in time.