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The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impacts of these technologies at the center of public debate.
Automated flower classification over a large number of classes
M-E. Nilsback and A. Zisserman · 2008
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Caltech-ucsd birds-200-2011 (cub-200-2011)
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Feedback control theory
John C Doyle, Bruce A Francis, and Allen R Tannenbaum · 2013
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Clean data for training statistical mt: the case of mt contamination
Michel Simard · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Degenerate feedback loops in recommender systems
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, and Pushmeet Kohli · 2019
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Generative adversarial networks in ai-enabled safety-critical systems: Friend or foe?
Apostolos P. Fournaris, Aris S. Lalos, and Dimitrios Serpanos · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Feedback loop and bias amplification in recommender systems
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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LAION-400M: Open dataset of CLIP-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
Cited alongside, same era.
Diffusion models: toward state-of-the-art image generation
Sergios Karagiannakos and Nikolaos Adaloglou · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Will large-scale generative models corrupt future datasets?
Ryuichiro Hataya, Han Bao, and Hiromi Arai · 2022
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Text-to-image diffusion models in generative ai: A survey, 2023
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon · 2023
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ChatGPT is not all you need. a state of the art review of large generative AI models
Roberto Gozalo-Brizuela and Eduardo C. Garrido-Merchan · 2023
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Fatemeh Fahimi, Strahinja Dosen, Kai Keng Ang, Natalie Mrachacz-Kersting, and Cuntai Guan · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
What are diffusion models?
Lilian Weng · 2021
Cited alongside, same era.
Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 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 Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
Cited alongside, same era.
A systematic review on data scarcity problem in deep learning: Solution and applications
Ms. Aayushi Bansal, Dr. Rewa Sharma, and Dr. Mamta Kathuria · 2022
Cited alongside, same era.
Geometry-based molecular generation with deep constrained variational autoencoder
Chunyan Li, Junfeng Yao, Wei Wei, Zhangming Niu, Xiangxiang Zeng, Jin Li, and Jianmin Wang · 2022
Cited alongside, same era.
Autoencoder in autoencoder networks
Changqing Zhang, Yu Geng, Zongbo Han, Yeqing Liu, Huazhu Fu, and Qinghua Hu · 2022
Cited alongside, same era.
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models, 2023
Riccardo Corvi, Davide Cozzolino, Giovanni Poggi, Koki Nagano, and Luisa Verdoliva · 2023
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Combining generative artificial intelligence (ai) and the internet: Heading towards evolution or degradation?, 2023
Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, and Rik Sarkar · 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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Text-to-image diffusion model in generative ai: A survey
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon · 2023
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Evading watermark based detection of ai-generated content
Zhengyuan Jiang, Jinghuai Zhang, and Neil Zhenqiang Gong · 2023
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The curse of recursion: Training on generated data makes models forget, 2023
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2023
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