mlsanityData to 14 Oct 2026

Continual Learning in Generative Adversarial Nets

arXiv 1705.08395 ↗First posted 23 May 2017cs.LG cs.AI stat.MLcited by 51

Not on the map. Why?

Abstract (arXiv)

Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 20; 2022: 9; 2023: 16; 2024: 4; 2025: 2.

51 papers.

Sort:

By area:

  1. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  2. 2021Rainbow Memory: Continual Learning with a Memory of Diverse Samplescited by 273
  3. 2023Continual Pre-training of Language Modelscited by 198
  4. 2023Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Modelscited by 177
  5. 2023Continual Pre-Training of Large Language Models: How to (re)warm your model?cited by 137
  6. 2021Achieving Forgetting Prevention and Knowledge Transfer in Continual Learningcited by 91
  7. 2021Continual Learning of a Mixed Sequence of Similar and Dissimilar Taskscited by 81
  8. 2022A Theoretical Study on Solving Continual Learningcited by 80
  9. 2021Rehearsal revealed: The limits and merits of revisiting samples in continual learningcited by 76
  10. 2021Recent Advances of Continual Learning in Computer Vision: An Overviewcited by 55
  11. 2021StyleAlign: Analysis and Applications of Aligned StyleGAN Modelscited by 48
  12. 2021Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Taskscited by 46
38 more
  1. 2021EEC: Learning to Encode and Regenerate Images for Continual Learningcited by 40
  2. 2022Continual Training of Language Models for Few-Shot Learningcited by 33
  3. 2023Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillationcited by 29
  4. 2023Learnability and Algorithm for Continual Learningcited by 28
  5. 2023Dealing with Cross-Task Class Discrimination in Online Continual Learningcited by 24
  6. 2022Continual Learning Based on OOD Detection and Task Maskingcited by 21
  7. 2021Lifelong Teacher-Student Network Learningcited by 18
  8. 2021Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generationcited by 14
  9. 2023Domain Expansion of Image Generatorscited by 13
  10. 2021The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Modelscited by 12
  11. 2021Continual Learning with Knowledge Transfer for Sentiment Classificationcited by 12
  12. 2023NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learningcited by 12
  13. 2022Beyond Supervised Continual Learning: a Reviewcited by 11
  14. 2021Continual learning in cross-modal retrievalcited by 10
  15. 2023Cost-effective On-device Continual Learning over Memory Hierarchy with Mirocited by 10
  16. 2021GAN Cocktail: mixing GANs without dataset accesscited by 8
  17. 2022Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approachcited by 8
  18. 2023Model Inversion Attack via Dynamic Memory Learningcited by 7
  19. 2023Sub-network Discovery and Soft-masking for Continual Learning of Mixed Taskscited by 7
  20. 2021DO-GAN: A Double Oracle Framework for Generative Adversarial Networkscited by 6
  21. 2022Continual Learning of Dynamical Systems with Competitive Federated Reservoir Computingcited by 6
  22. 2024CLoG: Benchmarking Continual Learning of Image Generation Modelscited by 5
  23. 2024CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networkscited by 4
  24. 2022Lifelong Generative Learning via Knowledge Reconstructioncited by 2
  25. 2023Mode-Aware Continual Learning for Conditional Generative Adversarial Networkscited by 2
  26. 2023Dynamically Masked Discriminator for Generative Adversarial Networkscited by 2
  27. 2023Look-Ahead Selective Plasticity for Continual Learning of Visual Taskscited by 2
  28. 2024Towards Lifelong Few-Shot Customization of Text-to-Image Diffusioncited by 2
  29. 2025NAACL2025 Tutorial: Adaptation of Large Language Modelscited by 2
  30. 2021Incremental Class Learning using Variational Autoencoders with Similarity Learningcited by 1
  31. 2021Reviewing continual learning from the perspective of human-level intelligencecited by 1
  32. 2021ClaRe: Practical Class Incremental Learning By Remembering Previous Class Representationscited by 0
  33. 2021Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learningcited by 0
  34. 2021CSG0: Continual Urban Scene Generation with Zero Forgettingcited by 0
  35. 2022Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Modelcited by 0
  36. 2022Overcoming Catastrophic Forgetting by XAIcited by 0
  37. 2023Continual Facial Expression Recognition: A Benchmarkcited by 0
  38. 2024Continual Learning of Personalized Generative Face Models with Experience Replaycited by 0

50 most cited of 51 All 51 (JSON)