mlsanityData to 14 Oct 2026

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

arXiv 2106.06819 ↗First posted 12 Jun 2021cs.LG cs.AI cs.CVcited by 96

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Cited by 70 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality. D2C can adapt to novel generation tasks conditioned on labels or manipulation constraints, by learning from as few as 100 labeled examples. On conditional generation from new labels, D2C achieves superior performance over state-of-the-art VAEs and diffusion models. On conditional image manipulation, D2C generations are two orders of magnitude faster to produce over StyleGAN2 ones and are preferred by 50% - 60% of the human evaluators in a double-blind study.

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). Builds on 90 of its 107 references that are in the map. First posted on arXiv 12 Jun 2021.

Builds on

90 of 107 references.

References · 90

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38 more
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50 most cited of 90 All 90 (JSON)

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Citing papers per year: 2021: 5; 2022: 28; 2023: 34; 2024: 16; 2025: 9; 2026: 4.

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  9. 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
  10. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagerycited by 131
  11. 2023Affordance Diffusion: Synthesizing Hand-Object Interactionscited by 110
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  18. 2022Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Modelscited by 54
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  20. 2022JPEG Artifact Correction using Denoising Diffusion Restoration Modelscited by 47
  21. 2023AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensicscited by 46
  22. 2022BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesiscited by 44
  23. 2024Diffusion Facial Forgery Detectioncited by 41
  24. 2022Few-Shot Diffusion Modelscited by 38
  25. 2023DiffComplete: Diffusion-based Generative 3D Shape Completioncited by 35
  26. 2023Addressing Negative Transfer in Diffusion Modelscited by 33
  27. 2024Attacks and Defenses for Generative Diffusion Models: A Comprehensive Surveycited by 32
  28. 2023UniGS: Unified Representation for Image Generation and Segmentationcited by 29
  29. 2023GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generationcited by 25
  30. 2022Few-shot Image Generation with Diffusion Modelscited by 21
  31. 2022Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generationcited by 20
  32. 2023A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shotcited by 19
  33. 2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
  34. 2023A Complete Recipe for Diffusion Generative Modelscited by 17
  35. 2023Realistic Human Motion Generation with Cross-Diffusion Modelscited by 17
  36. 2024DiffusionPen: Towards Controlling the Style of Handwritten Text Generationcited by 16
  37. 2023DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Datacited by 15
  38. 2024Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Modelscited by 15

50 most cited of 96 All 96 (JSON)

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