Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension

Maximum likelihood estimation of intrinsic dimension

Elizaveta Levina and Peter J. Bickel · 2004

Earlier work this paper cites.

The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality

Laurent Amsaleg, James Bailey, Dominique Barbe, Sarah Erfani, Michael E Houle, Vinh Nguyen, and Miloš Radovanović · 2017

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Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Elena Facco, Maria d’Errico, Alex Rodriguez, and Alessandro Laio · 2017

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Intrinsic dimension of data representations in deep neural networks

Alessio Ansuini, Alessandro Laio, Jakob H Macke, and Davide Zoccolan · 2019

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On the Intrinsic Dimensionality of Image Representations, April 2019

Sixue Gong, Vishnu Naresh Boddeti, and Anil K. Jain · 2019

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A Neural Scaling Law from the Dimension of the Data Manifold, April 2020

Utkarsh Sharma and Jared Kaplan · 2020

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Explaining Neural Scaling Laws, February 2021

Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2021

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The intrinsic dimension of images and its impact on learning

Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021

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Reproducible scaling laws for contrastive language-image learning, December 2022

Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2022

Cited alongside, same era.

Analyzing the latent space of gan through local dimension estimation

Original

Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, and Myungjoo Kang · 2022

Cited alongside, same era.

Intrinsic dimensionality estimation using Normalizing Flows

Christian Horvat and Jean-Pascal Pfister · 2022

Cited alongside, same era.

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 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, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 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 Laws for Generative Mixed-Modal Language Models, January 2023

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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Broken Neural Scaling Laws, January 2023

Ethan Caballero, Kshitij Gupta, Irina Rish, and David Krueger · 2023

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