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

@inproceedings{cvpr2020_msg_15864,
  title = {{MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2017_triple_56742,
  title = {{Triple Generative Adversarial Nets}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9189},
}

@inproceedings{neurips2017_stabilizing_56683,
  title = {{Stabilizing Training of Generative Adversarial Networks through Regularization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/8990},
}

@inproceedings{icml2017_generalization_36203,
  title = {{Generalization and Equilibrium in Generative Adversarial Nets (GANs)}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/854},
}

@inproceedings{icml2018_optimizing_36906,
  title = {{Optimizing the Latent Space of Generative Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2235},
}

@inproceedings{iclr2019_gan_28975,
  title = {{GAN Dissection: Visualizing and Understanding Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1089},
}

@inproceedings{neurips2018_pacgan_57499,
  title = {{PacGAN: The power of two samples in generative adversarial networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11165},
}

@inproceedings{cvpr2022_ensembling_18767,
  title = {{Ensembling Off-the-Shelf Models for GAN Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2021_gan_641,
  title = {{GAN Ensemble for Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/04090-gan-ensemble-for-anomaly-detection/},
}

@inproceedings{aaai2023_diffusion_3470,
  title = {{Diffusion Models Beat GANs on Topology Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26093},
}

@inproceedings{aaai2020_distilling_101905,
  title = {{Distilling Portable Generative Adversarial Networks for Image Translation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5765},
}

@inproceedings{neurips2018_on_57457,
  title = {{On GANs and GMMs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11569},
}

@inproceedings{icml2020_on_38537,
  title = {{On Leveraging Pretrained GANs for Generation with Limited Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/5942},
}

@inproceedings{cvpr2019_non_14576,
  title = {{Non-Adversarial Image Synthesis With Generative Latent Nearest Neighbors}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2017_enabling_105713,
  title = {{Enabling Dark Energy Science with Deep Generative Models of Galaxy Images}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10755},
}

@inproceedings{aaai2021_exgan_550,
  title = {{ExGAN: Adversarial Generation of Extreme Samples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/06750-exgan-adversarial-generation-of-extreme-samples/},
}

@inproceedings{aaai2020_degan_101849,
  title = {{DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5709},
}

@inproceedings{cvpr2020_noise_15952,
  title = {{Noise Robust Generative Adversarial Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2018_adversarial_36502,
  title = {{Adversarial Learning with Local Coordinate Coding}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/1902},
}

@inproceedings{cvpr2021_hijack_17122,
  title = {{Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{aaai2024_rg_7053,
  title = {{RG-GAN: Dynamic Regenerative Pruning for Data-Efficient Generative Adversarial Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28271},
}

@inproceedings{aaai2024_compressing_5454,
  title = {{Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29100},
}

@inproceedings{aaai2024_consistency_5474,
  title = {{Consistency-GAN: Training GANs with Consistency Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29503},
}

@inproceedings{aaai2025_diverse_8781,
  title = {{Diverse Rare Sample Generation with Pretrained GANs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32480},
}

@inproceedings{neurips2026_when_91108,
  title = {{When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152854},
}

@inproceedings{aaai2026_meta_107446,
  title = {{Meta-GAIN for Missing Data Imputation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39798},
}

@inproceedings{icml2024_bridging_44344,
  title = {{Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34108},
}