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Large training sets have become a cornerstone of machine learning and are the foundation for recent advances in language modeling and multimodal learning.
A generalized scanner for pattern- and character-recognition studies
W. H. Highleyman and L. A. Kamentsky · 1959
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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The mnist database of handwritten digits, 1998
Yann LeCun, Yann LeCun, and Yann LeCun · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Deep learning scaling is predictable, empirically, 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering, 2019
Drew A. Hudson and Christopher D. Manning · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 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
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Ccnet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut · 2021
Cited alongside, same era.
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Robust fine-tuning of zero-shot models, 2022
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt · 2022
Later among the works it cites.
Scaling laws for generative mixed-modal language models
Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, and Luke Zettlemoyer · 2023
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Getting vit in shape: Scaling laws for compute-optimal model design
Ibrahim Alabdulmohsin, Xiaohua Zhai, Alexander Kolesnikov, and Lucas Beyer · 2023
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axlearn, July 2023
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Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision, 2021
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
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
Cited alongside, same era.
“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo · 2021
Cited alongside, same era.
LAION-400M: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Cited alongside, same era.
Coyo-700m: Image-text pair dataset
Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim · 2022
Cited alongside, same era.
Data determines distributional robustness in contrastive language image pre-training (clip), 2022
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt · 2022
Cited alongside, same era.
Multimodal masked autoencoders learn transferable representations
Xinyang Geng, Hao Liu, Lisa Lee, Dale Schuurams, Sergey Levine, and Pieter Abbeel · 2022
Cited alongside, same era.
Apple · 2023
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Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Releasing Persimmon-8B, 2023
Erich Elsen, Augustus Odena, Maxwell Nye, Sağnak Taşırlar, Tri Dao, Curtis Hawthorne, Deepak Moparthi, and Arushi Somani · 2023
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Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al · 2023
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Quality not quantity: On the interaction between dataset design and robustness of clip, 2023
Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, and Ludwig Schmidt · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Combined scaling for zero-shot transfer learning, 2023
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, and Quoc V. Le · 2023
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Perceptual grouping in contrastive vision-language models, 2023
Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens · 2023
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Demystifying clip data, 2023
Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer · 2023
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Sigmoid loss for language image pre-training, 2023
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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