mlsanityData to 7 Oct 2026

High-Fidelity Image Generation With Fewer Labels

arXiv 1903.02271 ↗First posted 6 Mar 2019cs.LG cs.CV stat.MLcited by 51

Not on the map. Why?

Abstract (arXiv)

Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 14; 2022: 13; 2023: 8; 2024: 6; 2025: 7; 2026: 2.

51 papers.

Sort:

By area:

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2022Flow Matching for Generative Modelingcited by 5,426
  3. 2022MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesiscited by 222
  4. 2021Instance-Conditioned GANcited by 119
  5. 2023Return of Unconditional Generation: A Self-supervised Representation Generation Methodcited by 95
  6. 2024Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectivecited by 88
  7. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  8. 2021Is Attention Better Than Matrix Decomposition?cited by 75
  9. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
  10. 2021High Fidelity Visualization of What Your Self-Supervised Representation Knows Aboutcited by 64
  11. 2021What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labelscited by 63
  12. 2024Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Modelscited by 46
38 more
  1. 2023Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Trainingcited by 39
  2. 2022Self-Distilled StyleGAN: Towards Generation from Internet Photoscited by 37
  3. 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labelscited by 37
  4. 2022Self-Guided Diffusion Modelscited by 36
  5. 2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
  6. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  7. 2023Progressive Open Space Expansion for Open-Set Model Attributioncited by 32
  8. 2021Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspectivecited by 25
  9. 2024Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learningcited by 20
  10. 2021Class Balancing GAN with a Classifier in the Loopcited by 17
  11. 2024MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generationcited by 10
  12. 2024Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detectioncited by 10
  13. 2021Partition-Guided GANscited by 9
  14. 2021Self-Supervised GANs with Label Augmentationcited by 9
  15. 2023Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentationcited by 9
  16. 2021Model Extraction and Defenses on Generative Adversarial Networkscited by 8
  17. 2022Improved Input Reprogramming for GAN Conditioningcited by 7
  18. 2020Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environmentscited by 6
  19. 2022LANIT: Language-Driven Image-to-Image Translation for Unlabeled Datacited by 5
  20. 2025Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigationcited by 5
  21. 2025AI-Generated Image Detection: An Empirical Study and Future Research Directionscited by 5
  22. 2022OSSGAN: Open-Set Semi-Supervised Image Generationcited by 4
  23. 2022Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networkscited by 3
  24. 2023A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labelscited by 3
  25. 2025Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detectioncited by 2
  26. 2026Temporal Pair Consistency for Variance-Reduced Flow Matchingcited by 2
  27. 2021A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attentioncited by 1
  28. 2022A Survey of Learning on Small Data: Generalization, Optimization, and Challengecited by 1
  29. 2023Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variationscited by 1
  30. 2024Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradientcited by 1
  31. 2025Few-Shot Class-Incremental Model Attribution Using Learnable Representation From CLIP-ViT Featurescited by 1
  32. 2026DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalitiescited by 1
  33. 2022Generative Modeling Helps Weak Supervision (and Vice Versa)cited by 0
  34. 2022How does the degree of novelty impacts semi-supervised representation learning for novel class retrieval?cited by 0
  35. 2022Unifying conditional and unconditional semantic image synthesis with OCO-GANcited by 0
  36. 2023Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Datacited by 0
  37. 2025Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesiscited by 0
  38. 2025MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detectioncited by 0

50 most cited of 51 All 51 (JSON)