D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

By A. Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon. 2021

32 citations recorded by the source corpus.

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In Generative Models

Citations in the corpus

Score-based Generative Modeling in Latent Space (2021, background): “However, [10, 69, 70] consider the discretized diffusion objective [1], while we build on the continuous time framework.”
Riemannian Score-Based Generative Modeling (2022, methodology): “In the Euclidean setting this idea has been successfully applied for several image processing problems such as deblurring, denoising or inpainting (see for instance Kawar et al., 2021a; Kawar et al., 2021b; Lee et al., 2021; Sinha et al., 2021; Batzolis et al., 2021; Chung et al., 2021).”
Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (2023, methodology): “Variations of latent diffusion models have also been used for tasks such as controllable and semantic image generation [60, 77], and beyond image synthesis, such as 3D shape synthesis [107].”
LION: Latent Point Diffusion Models for 3D Shape Generation (2022, background): “[77] Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon.”
Perception Prioritized Training of Diffusion Models (2022, unknown): “FFHQ results reproduced from [11, 30, 35], Oxford Flower from [16], and CelebAHQ from from [42].”
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models (2022, background): “…performance, DPMs have shown great promise in a wide variety of applications, such as controllable generation (Choi et al., 2021; Meng et al., 2021; Sinha et al., 2021; Nichol et al., 2021), voice conversion (Popov et al., 2021), image super-resolution (Sa-haria et al., 2021; Li et al., 2021),…”