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Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models.
Mathematical Methods of Organizing and Planning Production
Leonid V Kantorovich · 1960
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Probability With Martingales
David Williams · 1991
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Bovine Spongiform Encephalopathy (BSE): Causes and Consequences of a Common Source Epidemic
Neal Nathanson, John Wilesmith, and Christian Griot · 1997
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Geometrical Foundations of Asymptotic Inference
Robert E Kass and Paul W Vos · 1997
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Niche News: The Politics of News Choice
Natalie Jomini Stroud · 2011
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The MNIST database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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Automating the news: How personalized news recommender system design choices impact news reception
Michael A Beam · 2014
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Exposure to ideologically diverse news and opinion on Facebook
Eytan Bakshy, Solomon Messing, and Lada A Adamic · 2015
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Down the (white) rabbit hole: The extreme right and online recommender systems
Derek O’Callaghan, Derek Greene, Maura Conway, Joe Carthy, and Pádraig Cunningham · 2015
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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The dark side of technology: An experimental investigation of the influence of customizability technology on online political selective exposure
Ivan Dylko, Igor Dolgov, William Hoffman, Nicholas Eckhart, Maria Molina, and Omar Aaziz · 2017
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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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Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity
Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 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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Deepfake detection by analyzing convolutional traces
Luca Guarnera, Oliver Giudice, and Sebastiano Battiato · 2020
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Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon JD Prince, and Marcus A Brubaker · 2020
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Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, and Patrick Schwab · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
DiGress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
Closest in time.
Programming is hard-or at least it used to be: Educational opportunities and challenges of AI code generation
Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos · 2023
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Nearly 50 news websites are ‘AI-generated’, a study says. Would I be able to tell?
Matthew Cantor · 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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AI spam is already flooding the internet and it has an obvious tell
Matthew Gault · 2023
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Cited alongside, same era.
AudioLM: a language modeling approach to audio generation
Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Olivier Teboul, David Grangier, Marco Tagliasacchi, and Neil Zeghidour · 2022
Cited alongside, same era.
First long-form speech synthesis platform for publishers and creators
ElevenLabs · 2022
Cited alongside, same era.
TabDDPM: Modelling tabular data with diffusion models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko · 2022
Cited alongside, same era.
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann et al · 2022
Cited alongside, same era.
Boomerang: Local sampling on image manifolds using diffusion models
Lorenzo Luzi, Ali Siahkoohi, Paul M Mayer, Josue Casco-Rodriguez, and Richard Baraniuk · 2022
Cited alongside, same era.
Kai Packhäuser, Lukas Folle, Florian Thamm, and Andreas Maier · 2022
Cited alongside, same era.
CNET secretly used AI on articles that didn’t disclose that fact, staff say
Jon Christian · 2023
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Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi · 2023
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Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes · 2023
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Leaving reality to imagination: Robust classification via generated datasets
Hritik Bansal and Aditya Grover · 2023
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Explore the power of synthetic data on few-shot object detection
Shaobo Lin, Kun Wang, Xingyu Zeng, and Rui Zhao · 2023
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Baize: An open-source chat model with parameter-efficient tuning on self-chat data
Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley · 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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A data augmentation perspective on diffusion models and retrieval
Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell · 2023
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LDFA: Latent diffusion face anonymization for self-driving applications
Marvin Klemp, Kevin Rösch, Royden Wagner, Jannik Quehl, and Martin Lauer · 2023
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The Economist , June 2023
The bigger-is-better approach to AI is running out of road · 2023
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The Economist , April 2023
Large, creative AI models will transform lives and labour markets · 2023
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Veniamin Veselovsky, Manoel Horta Ribeiro, and Robert West · 2023
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Toward understanding the impact of generative AI on future generative AI
Josue Casco-Rodriguez · 2023
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Toward understanding the impact of generative AI on future generative AI
Josue Casco-Rodriguez, Lorenzo Luzi, Sina Alemohammad, Shashank Sonkar, Ahmed Imtiaz Humayun, Ali Siahkoohi, and Richard Baraniuk · 2023
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The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson · 2023
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The power of synthetic data: Infinite loop to improve fine-tuning results with stable diffusion models
followfox.ai · 2023
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Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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A recipe for watermarking diffusion models
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Ngai-Man Cheung, and Min Lin · 2023
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Protecting the intellectual property of diffusion models by the watermark diffusion process
Sen Peng, Yufei Chen, Cong Wang, and Xiaohua Jia · 2023
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Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
Yuxin Wen, John Kirchenbauer, Jonas Geiping, and Tom Goldstein · 2023
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The stable signature: Rooting watermarks in latent diffusion models
Pierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze, and Teddy Furon · 2023
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