% Key papers for Meta-learning analysis and benchmarks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_meta_14511,
  title = {{Meta-Learning With Differentiable Convex Optimization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_meta_14513,
  title = {{Meta-Transfer Learning for Few-Shot Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2018_low_13348,
  title = {{Low-Shot Learning From Imaginary Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Low-Shot_Learning_From_CVPR_2018_paper.html},
}

@inproceedings{cvpr2022_learning_19235,
  title = {{Learning What Not To Segment: A New Perspective on Few-Shot Segmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2019_generating_14246,
  title = {{Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2019_image_14303,
  title = {{Image Deformation Meta-Networks for One-Shot Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2020_few_29403,
  title = {{Few-shot Text Classification with Distributional Signatures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=H1emfT4twB},
}

@inproceedings{cvpr2020_learning_15812,
  title = {{Learning to Forget for Meta-Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2020_rapid_29835,
  title = {{Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkgMkCEtPB},
}

@inproceedings{neurips2020_bayesian_59441,
  title = {{Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17645},
}

@inproceedings{iclr2020_meta_29833,
  title = {{Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=rkgAGAVKPr},
}

@inproceedings{neurips2019_unsupervised_59157,
  title = {{Unsupervised Meta-Learning for Few-Shot Image Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14034},
}

@inproceedings{iclr2021_boil_30713,
  title = {{BOIL: Towards Representation Change for Few-shot Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3347},
}

@inproceedings{iclr2021_melr_30110,
  title = {{MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3256},
}

@inproceedings{icml2019_lgm_37464,
  title = {{LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3808},
}

@inproceedings{aaai2021_few_615,
  title = {{Few-Shot One-Class Classification via Meta-Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07448-few-shot-one-class-classification-via-meta-learning/},
}

@inproceedings{cvpr2023_bi_20465,
  title = {{Bi-Level Meta-Learning for Few-Shot Domain Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21733},
}

@inproceedings{aaai2021_task_1467,
  title = {{Task Cooperation for Semi-Supervised Few-Shot Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/10682-task-cooperation-for-semi-supervised-few-shot-learning/},
}

@inproceedings{iclr2022_how_30853,
  title = {{How to Train Your MAML to Excel in Few-Shot Classification}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6294},
}

@inproceedings{icml2020_unraveling_38909,
  title = {{Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6591},
}

@inproceedings{cvpr2025_task_27828,
  title = {{Task-Specific Gradient Adaptation for Few-Shot One-Class Classification}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33957},
}

@inproceedings{icml2024_unleashing_44382,
  title = {{Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34070},
}

@inproceedings{neurips2025_moemeta_77112,
  title = {{MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=QhKnVxk96T},
}

@inproceedings{icml2024_enabling_44166,
  title = {{Enabling Few-Shot Learning with PID Control: A Layer Adaptive Optimizer}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34286},
}

@inproceedings{iclr2025_why_83106,
  title = {{Why In-Context Learning Models are Good Few-Shot Learners?}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28701},
}

@inproceedings{cvpr2026_data_98770,
  title = {{Data-Centric Meta-Learning for Robust Few-Shot Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Lim_Data-Centric_Meta-Learning_for_Robust_Few-Shot_Generalization_CVPR_2026_paper.html},
}