mlsanityData to 14 Oct 2026

Manifold Learning Benefits GANs

arXiv 2112.12618 ↗First posted 23 Dec 2021cs.CV cs.LGcited by 14

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Abstract (arXiv)

In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds, and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discriminator, with the goal of attracting intermediate feature representations onto manifolds. We adaptively balance the discrepancy between feature representations and their manifold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear manifolds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 37 of its 73 references that are in the map. First posted on arXiv 23 Dec 2021.

Builds on

37 of 73 references.

References · 37

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  4. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  5. 2016Improved Techniques for Training GANscited by 2,970
  6. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  7. 2017Improved Training of Wasserstein GANscited by 2,235
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  9. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  10. 2020Training Generative Adversarial Networks with Limited Datacited by 1,186
  11. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  12. 2018Self-Attention Generative Adversarial Networkscited by 809
25 more
  1. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 363
  2. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  3. 2017MMD GAN: Towards Deeper Understanding of Moment Matching Networkcited by 254
  4. 2018cGANs with Projection Discriminatorcited by 254
  5. 2018Adversarial Audio Synthesiscited by 225
  6. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  7. 2017Optimizing the Latent Space of Generative Networkscited by 157
  8. 2019Consistency Regularization for Generative Adversarial Networkscited by 136
  9. 2017Stabilizing Training of Generative Adversarial Networks through Regularizationcited by 118
  10. 2021Regularizing Generative Adversarial Networks under Limited Datacited by 107
  11. 2020Improved Consistency Regularization for GANscited by 69
  12. 2018Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effectcited by 49
  13. 2021Training GANs with Stronger Augmentations via Contrastive Discriminatorcited by 45
  14. 2019Detecting Overfitting of Deep Generative Networks via Latent Recoverycited by 44
  15. 2019Spectral Regularization for Combating Mode Collapse in GANscited by 33
  16. 2018Disconnected Manifold Learning for Generative Adversarial Networkscited by 27
  17. 2020Smoothness and Stability in GANscited by 27
  18. 2020Feature Quantization Improves GAN Trainingcited by 23
  19. 2020Instance Selection for GANscited by 23
  20. 2017McGan: Mean and Covariance Feature Matching GANcited by 21
  21. 2020Omni-GAN: On the Secrets of cGANs and Beyondcited by 11
  22. 2019Identity-preserving Face Recovery from Stylized Portraitscited by 9
  23. 2019Recovering Faces from Portraits with Auxiliary Facial Attributescited by 6
  24. 2018Identity-preserving Face Recovery from Portraitscited by 2
  25. 2018Face Destylizationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 2; 2026: 3.

14 papers.

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  1. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  2. 2024PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationcited by 14
  3. 2023Knowledge Distillation in Vision Transformers: A Critical Reviewcited by 13
  4. 2022Multi-manifold Attention for Vision Transformerscited by 12
  5. 2023VTAE: Variational Transformer Autoencoder with Manifolds Learningcited by 10
  6. 2024Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignmentcited by 10
  7. 2024CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalizationcited by 2
  8. 2025Text-to-3D Generation using Jensen-Shannon Score Distillationcited by 2
  9. 2025Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problemscited by 1
  10. 2023Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Datacited by 0
  11. 2024Deep Extrinsic Manifold Representation for Vision Taskscited by 0
  12. 2026Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Modelcited by 0
2 more
  1. 2026Possibilistic Predictive Uncertainty for Deep Learningcited by 0
  2. 2026Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generationcited by 0