# tata1661/FSL-Mate

FSL-Mate: A collection of resources for few-shot learning (FSL).

Repository: https://github.com/tata1661/FSL-Mate
Canonical: https://ross.abutalabs.com/products/fsl-mate
Language: Python
License Family: other
Topics: few-shot-learning, one-shot-learning, meta-learning, low-shot, deep-learning, papers, few-shot-paper, few-shot-papers, paper, paddlepaddle, few-shot
Last push: 2026-04-18T03:48:50+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 35, longevity 100
- inputs: {"age_days": 2349, "days_push": 137, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1764, forks 287 (observed 2026-08-28T04:05:32.995081+00:00)

## What it is
FSL-Mate is a curated collection of resources for few-shot learning, containing a tracked paper list (FewShotPapers) and a PaddlePaddle-based Python library (PaddleFSL). It is regularly updated with papers from major ML and CV/NLP conferences.

## Use cases
- find recent few-shot learning papers
- track meta-learning research advances
- get started with few-shot learning in Python
- find one-shot learning survey material
- run few-shot learning experiments with PaddlePaddle
- keep up with FSL papers from CVPR, NeurIPS, ACL

## When to choose
- you need an up-to-date curated list of few-shot learning literature
- you want a PaddlePaddle-based library for FSL experiments
- you are surveying meta-learning or low-shot research

## When to avoid
- you need a production-ready few-shot learning framework in PyTorch or TensorFlow
- you want a maintained library with a clear license for commercial use
- you only need general-purpose deep learning tooling

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, documentation
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: python
- tags: few-shot-learning, meta-learning, one-shot-learning, paper-list, paddlepaddle, research

## Member repositories
- tata1661/FSL-Mate (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:32.995081+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:26:41.564746+00:00, confidence not recorded.
  - readme: https://github.com/tata1661/FSL-Mate (fetched 2026-08-28T04:05:32.995081+00:00, sha ae9ed1086801)
- Data as of 2026-08-30T08:39:29.467469+00:00.