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FSL-Mate: A collection of resources for few-shot learning (FSL).
| Date | Stars |
|---|---|
| 2026-07-24 | 1763 |
| 2026-07-25 | 1763 |
| 2026-07-28 | 1763 |
| 2026-07-30 | 1763 |
| 2026-08-06 | 1763 |
Today
— stars today
This week
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This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center"><img src="logo-fsl-mate.png" alt="logo" width="400px" /></p>
---
**FSL-Mate** is a collection of resources for few-shot learning (FSL).
In particular, FSL-Mate currently contains
- [**FewShotPapers**](https://github.com/tata1661/FSL-Mate/tree/master/FewShotPapers): a paper list which tracks the research advances on FSL
- [**PaddleFSL**](https://github.com/tata1661/FSL-Mate/tree/master/PaddleFSL): a PaddlePaddle-based python library for FSL
We are endeavored to constantly update FSL-Mate. Hopefully, it can make FSL easier.
## News🔥
- [2026-04-18] Add FSL papers published in EMNLP 2025 and ICCV 2025, ICML 2025, WWW 2026 and AAAI 2026.
- [2025-10-14] Add FSL papers published in CVPR 2025, ACL 2025 and IJCAI 2025.
- [2025-07-17] Add FSL papers published in CVPR 2024, ICML 2024, IJCAI 2024, ACL 2024, NeurIPS 2024, EMNLP 2024, ICCV 2024, ICLR 2025, WWW 2024-2025, KDD 2024-2025, AAAI 2024-2025, NAACL 2024-2025, SIGIR 2024-2025.
## Cite Us
Please cite our [paper](https://dl.acm.org/doi/10.1145/3386252?cid=99659542534) if you find it helpful.
```
@article{wang2020generalizing,
title={Generalizing from a few examples: A survey on few-shot learning},
author={Wang, Yaqing and Yao, Quanming and Kwok, James T and Ni, Lionel M},
journal={ACM Computing Surveys},
volume={53},
number={3},
pages={1--34},
year={2020},
publisher={ACM New York, NY, USA}
}
```
## Contact
We welcome advices and feedbacks for FSL-Mate. Please feel free to open an issue or contact [Yaqing Wang](mailto:[email protected]).
Excerpt of 1,564 characters
Read on GitHub
Yaqing Wang
54
骑马小猫 · @baidu · China
22
11
5
5
3
2
2
Arjun Ashok · Canada
1
Zeyu Chen · Baidu, Inc. · China
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d746411a87e03863, topic:papers, desc:collection of resources, readme:collection of resources
matched fp:d746411a87e03863, topic:deep-learning