% Key papers for Deep one-class and tabular anomaly detection (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2018_deep_36623,
  title = {{Deep One-Class Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2483},
}

@inproceedings{cvpr2018_adversarially_12827,
  title = {{Adversarially Learned One-Class Classifier for Novelty Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Sabokrou_Adversarially_Learned_One-Class_CVPR_2018_paper.html},
}

@inproceedings{cvpr2022_self_19833,
  title = {{Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2020_old_15971,
  title = {{Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2020_timeseries_60977,
  title = {{Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18594},
}

@inproceedings{cvpr2024_promptad_24535,
  title = {{PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29202},
}

@inproceedings{aaai2022_deep_1941,
  title = {{Deep One-Class Classification via Interpolated Gaussian Descriptor}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/00383-deep-one-class-classification-via-interpolated-gaussian-descriptor/},
}

@inproceedings{iclr2021_explainable_30077,
  title = {{Explainable Deep One-Class Classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2521},
}

@inproceedings{aaai2024_logformer_6543,
  title = {{LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27764},
}

@inproceedings{icml2019_anomaly_37159,
  title = {{Anomaly Detection With Multiple-Hypotheses Predictions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3586},
}

@inproceedings{aaai2021_deep_401,
  title = {{Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07002-deep-verifier-networks-verification-of-deep-discriminative-models-with-deep-generative-models/},
}

@inproceedings{aaai2023_one_4294,
  title = {{One-for-All: Proposal Masked Cross-Class Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25604},
}

@inproceedings{iclr2020_deep_29499,
  title = {{Deep Semi-Supervised Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HkgH0TEYwH},
}

@inproceedings{aaai2024_dense_5666,
  title = {{Dense Projection for Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28682},
}

@inproceedings{neurips2022_perturbation_63653,
  title = {{Perturbation Learning Based Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54593},
}

@inproceedings{cvpr2025_distribution_25973,
  title = {{Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35073},
}

@inproceedings{cvpr2018_multi_13391,
  title = {{Multi-Cell Detection and Classification Using a Generative Convolutional Model}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Yellin_Multi-Cell_Detection_and_CVPR_2018_paper.html},
}

@inproceedings{icml2021_non_39670,
  title = {{Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8605},
}

@inproceedings{icml2023_fascinating_42381,
  title = {{Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24705},
}

@inproceedings{aaai2025_disentangling_8765,
  title = {{Disentangling Tabular Data Towards Better One-Class Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33425},
}

@inproceedings{aaai2025_unsupervised_11179,
  title = {{Unsupervised Anomaly Detection for Tabular Data Using Deep Noise Evaluation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33257},
}

@inproceedings{neurips2025_normal_78370,
  title = {{Normal-Abnormal Guided Generalist Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=dv6X6sv18b},
}

@inproceedings{aaai2024_skip_7354,
  title = {{Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30496},
}

@inproceedings{icml2024_beyond_44869,
  title = {{Beyond Individual Input for Deep Anomaly Detection on Tabular Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33583},
}

@inproceedings{neurips2024_dmnet_73775,
  title = {{DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93731},
}

@inproceedings{aaai2026_robust_107070,
  title = {{Robust Detection of Synthetic Tabular Data Under Schema Variability}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39422},
}

@inproceedings{iclr2024_deep_34773,
  title = {{Deep Orthogonal Hypersphere Compression for Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18301},
}

@inproceedings{icml2026_towards_53523,
  title = {{Towards One-for-All Anomaly Detection for Tabular Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=gHA60hnmOK},
}

@inproceedings{neurips2025_unifying_80249,
  title = {{Unifying Reconstruction and Density Estimation via Invertible Contraction Mapping in One-Class Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xju2uBgTrB},
}

@inproceedings{iclr2025_one_82607,
  title = {{One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29190},
}

@inproceedings{neurips2026_flextab_90499,
  title = {{FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/153378},
}

@inproceedings{neurips2026_local_92535,
  title = {{Local Sparsity Enables Unsupervised LLM Safety Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154110},
}

@inproceedings{cvpr2026_mmr_95983,
  title = {{MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Yao_MMR-AD_A_Large-Scale_Multimodal_Dataset_for_Benchmarking_General_Anomaly_Detection_CVPR_2026_paper.html},
}

@inproceedings{aaai2026_misf_106164,
  title = {{MISF: MLLM Guided Iterative Sample Filtering for Data Fault Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38471},
}

@inproceedings{iclr2024_mcm_35141,
  title = {{MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17932},
}