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Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross B. Girshick, and Kaiming He. 2020b · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Learning multiple layers of features from tiny images
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell. 2010 · 2010
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014 · 2014
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Microsoft COCO captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C. Lawrence Zitnick. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Adaptive data augmentation for image classification
Alhussein Fawzi, Horst Samulowitz, Deepak S. Turaga, and Pascal Frossard. 2016 · 2016
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Deep Learning
Ian J. Goodfellow, Yoshua Bengio, and Aaron C. Courville. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala. 2016 · 2016
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos J. Storkey, and Harrison Edwards. 2017 · 2017
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The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang. 2017 · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. 2017 · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017 · 2017
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GAN augmentation: Augmenting training data using generative adversarial networks
Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger N. Gunn, Alexander Hammers, David Alexander Dickie, Maria del C. Valdés Hernández, Joanna M. Wardlaw, and Daniel Rueckert. 2018 · 2018
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Data augmentation for improving deep learning in image classification problem
Agnieszka Mikołajczyk and Michał Grochowski. 2018 · 2018
An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He. 2021 · 2021
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Guiding generative language models for data augmentation in few-shot text classification
Aleksandra Edwards, Asahi Ushio, José Camacho-Collados, Hélène de Ribaupierre, and Alun D. Preece. 2021 · 2021
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Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2021a · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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Throwing away data improves worst-class error in imbalanced classification
Martín Arjovsky, Kamalika Chaudhuri, and David Lopez-Paz. 2022 · 2022
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M. Khoshgoftaar. 2019 · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020a · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. 2020 · 2020
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Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2020 · 2020
Cited alongside, same era.
Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. 2020 · 2020
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, and David Lopez-Paz. 2022 · 2022
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Causal machine learning: A survey and open problems
Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva. 2022 · 2022
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mplug: Effective and efficient vision-language learning by cross-modal skip-connections
Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, and Luo Si. 2022 · 2022
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Spurious features everywhere - large-scale detection of harmful spurious features in imagenet
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, and Matthias Hein. 2022 · 2022
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GLIDE: towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
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Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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On the limitations of dataset balancing: The lost battle against spurious correlations
Roy Schwartz and Gabriel Stanovsky. 2022 · 2022
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When does dough become a bagel? analyzing the remaining mistakes on imagenet
Vijay Vasudevan, Benjamin Caine, Raphael Gontijo Lopes, Sara Fridovich-Keil, and Rebecca Roelofs. 2022 · 2022
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Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Tongkun Xu, Weihua Chen, Pichao Wang, Fan Wang, Hao Li, and Rong Jin. 2022 · 2022
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Spawrious: A benchmark for fine control of spurious correlation biases
Aengus Lynch, Gbètondji J.-S. Dovonon, Jean Kaddour, and Ricardo Silva. 2023 · 2023
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