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Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting curated data at scale.
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Automated flower classification over a large number of classes
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Generative adversarial nets
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Adam: A method for stochastic optimization
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On rendering synthetic images for training an object detector
Artem Rozantsev, Vincent Lepetit, and Pascal Fua · 2015
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
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Yfcc100m: The new data in multimedia research
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Remote sensing image scene classification: Benchmark and state of the art
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Deep learning scaling is predictable, empirically
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Neural discrete representation learning
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Attention is all you need
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Augmented reality meets computer vision: Efficient data generation for urban driving scenes
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Leveraging sequence-to-sequence speech synthesis for enhancing acoustic-to-word speech recognition
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Cross-domain self-supervised multi-task feature learning using synthetic imagery
Zhongzheng Ren and Yong Jae Lee · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach
Yuhua Chen, Wen Li, Xiaoran Chen, and Luc Van Gool · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Imagenet-21k pretraining for the masses
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How to train your vit? data, augmentation, and regularization in vision transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2021
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Revisiting neural scaling laws in language and vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, and Xiaohua Zhai · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Speech recognition with augmented synthesized speech
Andrew Rosenberg, Yu Zhang, Bhuvana Ramabhadran, Ye Jia, Pedro Moreno, Yonghui Wu, and Zelin Wu · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Pytorch image models
Ross Wightman · 2019
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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
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Generative adversarial networks (gan) based efficient sampling of chemical composition space for inverse design of inorganic materials
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An image is worth 16x16 words: Transformers for image recognition at scale
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Training compute-optimal large language models
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Palm up: Playing in the latent manifold for unsupervised pretraining
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Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han · 2022
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Lens: Localization enhanced by nerf synthesis
Arthur Moreau, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, and Arnaud de La Fortelle · 2022
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Slip: Self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie · 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
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2022
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Nerf-supervision: Learning dense object descriptors from neural radiance fields
Lin Yen-Chen, Pete Florence, Jonathan T Barron, Tsung-Yi Lin, Alberto Rodriguez, and Phillip Isola · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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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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Synthetic data from diffusion models improves imagenet classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J Fleet · 2023
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Improving image generation with better captions, 2023
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
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Diffusion models as artists: Are we closing the gap between humans and machines?
Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, and Thomas Serre · 2023
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Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al · 2023
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
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Scaling up gans for text-to-image synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park · 2023
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OpenAI · 2023
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Fake it till you make it: Learning transferable representations from synthetic imagenet clones
Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, and Yannis Kalantidis · 2023
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Alpaca: A strong, replicable instruction-following model
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Stablerep: Synthetic images from text-to-image models make strong visual representation learners
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, and Dilip Krishnan · 2023
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