% Key papers for Augmentation and diffusion purification (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_mixup_28653,
  title = {{mixup: Beyond Empirical Risk Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/177},
}

@inproceedings{cvpr2021_stylemix_17958,
  title = {{StyleMix: Separating Content and Style for Enhanced Data Augmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2020_adversarial_29394,
  title = {{Adversarial AutoAugment}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ByxdUySKvS},
}

@inproceedings{icml2020_puzzle_38659,
  title = {{Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6827},
}

@inproceedings{icml2023_better_41404,
  title = {{Better Diffusion Models Further Improve Adversarial Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24423},
}

@inproceedings{neurips2019_self_58965,
  title = {{Self-Routing Capsule Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13846},
}

@inproceedings{cvpr2022_pyramid_19642,
  title = {{Pyramid Adversarial Training Improves ViT Performance}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2022_patchup_2544,
  title = {{PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/00589-patchup-a-feature-space-block-level-regularization-technique-for-convolutional-neural-networks/},
}

@inproceedings{cvpr2023_cuda_20556,
  title = {{CUDA: Convolution-Based Unlearnable Datasets}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21010},
}

@inproceedings{cvpr2024_mimicdiffusion_24143,
  title = {{MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29293},
}

@inproceedings{neurips2021_augmax_62122,
  title = {{AugMax: Adversarial Composition of Random Augmentations for Robust Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26387},
}

@inproceedings{neurips2022_efficient_64790,
  title = {{Efficient and Effective Augmentation Strategy for Adversarial Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54553},
}

@inproceedings{cvpr2019_retrieval_14737,
  title = {{Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2023_diffattack_68062,
  title = {{DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71718},
}

@inproceedings{iclr2023_self_32163,
  title = {{Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10872},
}

@inproceedings{iclr2023_one_33190,
  title = {{One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11954},
}

@inproceedings{neurips2023_ipmix_67926,
  title = {{IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71841},
}

@inproceedings{icml2024_robust_45749,
  title = {{Robust Classification via a Single Diffusion Model}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32703},
}

@inproceedings{iclr2022_defending_31599,
  title = {{Defending Against Image Corruptions Through Adversarial Augmentations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6715},
}

@inproceedings{iclr2024_deepzero_35328,
  title = {{DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17745},
}

@inproceedings{neurips2024_puregen_72772,
  title = {{PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94623},
}

@inproceedings{icml2025_sample_46199,
  title = {{Sample-specific Noise Injection for Diffusion-based Adversarial Purification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46361},
}

@inproceedings{neurips2024_learning_74570,
  title = {{Learning Bregman Divergences with Application to Robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93016},
}

@inproceedings{neurips2025_adversary_77576,
  title = {{Adversary Aware Optimization for Robust Defense}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=VNbvk5pMS7},
}

@inproceedings{icml2025_not_46469,
  title = {{Not All Wrong is Bad: Using Adversarial Examples for Unlearning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46097},
}