% Key papers for Stabilized GAN training (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_spectral_28405,
  title = {{Spectral Normalization for Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/331},
}

@inproceedings{icml2019_self_37729,
  title = {{Self-Attention Generative Adversarial Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3774},
}

@inproceedings{aaai2017_seqgan_104427,
  title = {{SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10804},
}

@inproceedings{iclr2018_progressive_28529,
  title = {{Progressive Growing of GANs for Improved Quality, Stability, and Variation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/204},
}

@inproceedings{neurips2017_improved_56376,
  title = {{Improved Training of Wasserstein GANs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9350},
}

@inproceedings{neurips2020_training_61007,
  title = {{Training Generative Adversarial Networks with Limited Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17332},
}

@inproceedings{icml2018_gain_36708,
  title = {{GAIN: Missing Data Imputation using Generative Adversarial Nets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2025},
}

@inproceedings{iclr2019_the_29007,
  title = {{The relativistic discriminator: a key element missing from standard GAN}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/728},
}

@inproceedings{cvpr2018_deep_12978,
  title = {{Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Deep_Photo_Enhancer_CVPR_2018_paper.html},
}

@inproceedings{neurips2017_veegan_56760,
  title = {{VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9115},
}

@inproceedings{iclr2017_unrolled_28238,
  title = {{Unrolled Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=BydrOIcle},
}

@inproceedings{cvpr2020_a_15088,
  title = {{A U-Net Based Discriminator for Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2018_multi_13390,
  title = {{Multi-Agent Diverse Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Ghosh_Multi-Agent_Diverse_Generative_CVPR_2018_paper.html},
}

@inproceedings{cvpr2017_deligan_12141,
  title = {{DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{cvpr2018_st_13556,
  title = {{ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Lin_ST-GAN_Spatial_Transformer_CVPR_2018_paper.html},
}

@inproceedings{iclr2018_mgan_28703,
  title = {{MGAN: Training Generative Adversarial Nets with Multiple Generators}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/318},
}

@inproceedings{aaai2019_pgans_99944,
  title = {{PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3830},
}

@inproceedings{aaai2018_flow_102562,
  title = {{Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11829},
}

@inproceedings{neurips2020_improved_60058,
  title = {{Improved Techniques for Training Score-Based Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17969},
}

@inproceedings{neurips2017_dual_56267,
  title = {{Dual Discriminator Generative Adversarial Nets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9053},
}

@inproceedings{aaai2024_generative_6129,
  title = {{Generative Model Perception Rectification Algorithm for Trade-Off between Diversity and Quality}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29234},
}

@inproceedings{cvpr2024_structure_24902,
  title = {{Structure-Guided Adversarial Training of Diffusion Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30024},
}

@inproceedings{cvpr2024_taming_24957,
  title = {{Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31118},
}

@inproceedings{cvpr2024_adversarial_22736,
  title = {{Adversarial Score Distillation: When score distillation meets GAN}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30525},
}

@inproceedings{iclr2024_san_34862,
  title = {{SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18212},
}

@inproceedings{aaai2024_discriminative_5710,
  title = {{Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29013},
}

@inproceedings{aaai2024_turning_7662,
  title = {{Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29065},
}

@inproceedings{aaai2025_mcgan_9838,
  title = {{MCGAN: Enhancing GAN Training with Regression-Based Generator Loss}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35468},
}

@inproceedings{cvpr2024_chain_22911,
  title = {{CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30041},
}

@inproceedings{iclr2025_diffusing_83234,
  title = {{Diffusing States and Matching Scores: A New Framework for Imitation Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28575},
}

@inproceedings{cvpr2025_improving_26613,
  title = {{Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34024},
}

@inproceedings{icml2024_ms_44491,
  title = {{MS\$^3\$D: A RG Flow-Based Regularization for GAN Training with Limited Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33961},
}

@inproceedings{icml2024_smart_45252,
  title = {{SMaRt: Improving GANs with Score Matching Regularity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33200},
}

@inproceedings{icml2025_direct_47133,
  title = {{Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45454},
}

@inproceedings{iclr2026_verification_85172,
  title = {{Verification of the Implicit World Model in a Generative Model via Adversarial Sequences}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=BLOIB8CwBI},
}

@inproceedings{cvpr2026_generative_96074,
  title = {{Generative Modeling of Weights: Generalization or Memorization?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Zeng_Generative_Modeling_of_Weights_Generalization_or_Memorization_CVPR_2026_paper.html},
}

@inproceedings{icml2024_improving_44373,
  title = {{Improving Adversarial Energy-Based Model via Diffusion Process}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34079},
}

@inproceedings{iclr2025_do_80954,
  title = {{Do WGANs succeed because they minimize the Wasserstein Distance? Lessons from Discrete Generators}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30814},
}

@inproceedings{iclr2026_sona_88365,
  title = {{SONA: Learning Conditional, Unconditional, and Matching-Aware Discriminator}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=lymykMnKBS},
}

@inproceedings{neurips2026_magnitude_89866,
  title = {{Magnitude-preserving Layers Enable Efficient GANs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150733},
}