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We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models.
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Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2019
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Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
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Diffusion models beat gans on image synthesis
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Learning transferable visual models from natural language supervision
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Highly personalized text embedding for image manipulation by stable diffusion
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Enhanced balancing gan: Minority-class image generation
Gaofeng Huang and Amir Hossein Jafari · 2023
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Regularization by texts for latent diffusion inverse solvers
Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, and Jong Chul Ye · 2023
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Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
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Your diffusion model is secretly a zero-shot classifier
Alexander C Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
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An image is worth one word: Personalizing text-to-image generation using textual inversion
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Self-guided generation of minority samples using diffusion models
Soobin Um and Jong Chul Ye · 2024
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Iterative object count optimization for text-to-image diffusion models
Oz Zafar, Lior Wolf, and Idan Schwartz · 2024
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