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Evolutionary Generative Adversarial Networks

arXiv 1803.00657 ↗First posted 1 Mar 2018cs.LG cs.NE stat.MLcited by 42

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Abstract (arXiv)

Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2018.

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Citing papers per year: 2021: 13; 2022: 10; 2023: 7; 2024: 4; 2025: 4; 2026: 4.

42 papers.

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  1. 2022Modeling Image Composition for Complex Scene Generationcited by 43
  2. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  3. 2022Robust Unlearnable Examples: Protecting Data Against Adversarial Learningcited by 34
  4. 2021ViTAS: Vision Transformer Architecture Searchcited by 31
  5. 2023A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRUcited by 25
  6. 2022A Comprehensive Survey on Data-Efficient GANs in Image Generationcited by 20
  7. 2022FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANscited by 17
  8. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  9. 2022A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimizationcited by 7
  10. 2023RLocator: Reinforcement Learning for Bug Localizationcited by 7
  11. 2021EvoGAN: An Evolutionary Computation Assisted GANcited by 6
  12. 2023EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learningcited by 6
30 more
  1. 2021EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANscited by 5
  2. 2023A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)cited by 5
  3. 2023Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generationcited by 5
  4. 2022ScaleNet: Searching for the Model to Scalecited by 4
  5. 2022Leveraging GAN Priors for Few-Shot Part Segmentationcited by 4
  6. 2021Signal Propagation in a Gradient-Based and Evolutionary Learning Systemcited by 3
  7. 2021Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillationcited by 2
  8. 2021Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunitiescited by 2
  9. 2021A Data-driven Approach to Neural Architecture Search Initializationcited by 2
  10. 2021Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classificationcited by 2
  11. 2023OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML Systemcited by 2
  12. 2024CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4Vcited by 2
  13. 2022Evolving SimGANs to Improve Abnormal Electrocardiogram Classificationcited by 1
  14. 2025MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Maskingcited by 1
  15. 2021Fostering Diversity in Spatial Evolutionary Generative Adversarial Networkscited by 0
  16. 2021Parallelized Reverse Curriculum Generationcited by 0
  17. 2021IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operatorcited by 0
  18. 2021Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosiscited by 0
  19. 2021DGL-GAN: Discriminator Guided Learning for GAN Compressioncited by 0
  20. 2022Dynamics of Fourier Modes in Torus Generative Adversarial Networkscited by 0
  21. 2023See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imagingcited by 0
  22. 2024Simplicity within biological complexitycited by 0
  23. 2024Towards evolution of Deep Neural Networks through contrastive Self-Supervised learningcited by 0
  24. 2025Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approachcited by 0
  25. 2025Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learningcited by 0
  26. 2025Evolutionary Computation as Natural Generative AIcited by 0
  27. 2026Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentationcited by 0
  28. 2026Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysiscited by 0
  29. 2026Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANscited by 0
  30. 2026Adversarial Debiasing of Machine Learning Models for Enhanced Network Security against DDoS Attackscited by 0