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Deep generative models learn the data distribution, which is concentrated on a low-dimensional manifold.
A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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Poisson image editing
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Region filling and object removal by exemplar-based image inpainting
Antonio Criminisi, Patrick Pérez, and Kentaro Toyama · 2004
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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The manifold tangent classifier
Salah Rifai, Yann N Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller · 2011
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Representation learning: A review and new perspectives
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
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Cat2000: A large scale fixation dataset for boosting saliency research
Ali Borji and Laurent Itti · 2015
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The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P. Thomas Fletcher · 2017
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Semi-supervised learning with gans: Manifold invariance with improved inference
Abhishek Kumar, Prasanna Sattigeri, and Tom Fletcher · 2017
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Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2018
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Metrics for deep generative models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick Smagt · 2018
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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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A spectral regularizer for unsupervised disentanglement, 2019
Aditya Ramesh, Youngduck Choi, and Yann LeCun · 2019
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Huikai Wu, Shuai Zheng, Junge Zhang, and Kaiqi Huang · 2019
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Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Composite photograph harmonization with complete background cues
Yazhou Xing, Yu Li, Xintao Wang, Ye Zhu, and Qifeng Chen · 2022
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Diffusion models already have a semantic latent space
Mingi Kwon, Jaeseok Jeong, and Youngjung Uh · 2023
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Understanding the latent space of diffusion models through the lens of riemannian geometry
Yong-Hyun Park, Mingi Kwon, Jaewoong Choi, Junghyo Jo, and Youngjung Uh · 2023
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Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, and Liqing Zhang · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Emerging properties in self-supervised vision transformers
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Learning transferable visual models from natural language supervision
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Deep saliency prior for reducing visual distraction
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Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
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Zone: Zero-shot instruction-guided local editing
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A theoretical justification for image inpainting using denoising diffusion probabilistic models, 2023
Litu Rout, Advait Parulekar, Constantine Caramanis, and Sanjay Shakkottai · 2023
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Emu edit: Precise image editing via recognition and generation tasks
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Realistic saliency guided image enhancement
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Dragondiffusion: Enabling drag-style manipulation on diffusion models, 2023
Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang · 2023
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Instructpix2pix: Learning to follow image editing instructions
Tim Brooks, Aleksander Holynski, and Alexei A Efros · 2023
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Drag your gan: Interactive point-based manipulation on the generative image manifold
Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt · 2023
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Prompt-to-prompt image editing with cross-attention control
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Making images real again: A comprehensive survey on deep image composition, 2024
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