# sudharsan13296/Awesome-Meta-Learning

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

Repository: https://github.com/sudharsan13296/Awesome-Meta-Learning
Canonical: https://ross.abutalabs.com/products/awesome-meta-learning
License Family: other
Topics: metalearning, one-shot-learning, zero-shot-learning, few-shot-learning, deep-meta-learning, meta-reinforcement
Last push: 2020-11-24T09:32:33+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2638, "days_push": 2108, "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 1554, forks 296 (observed 2026-08-28T04:05:02.996599+00:00)

## What it is
A curated awesome-list of meta-learning resources including papers with code, books, libraries, blogs, lecture videos, datasets, and workshops. It covers zero-shot, one-shot, few-shot, and meta-reinforcement learning topics.

## Use cases
- find papers on few-shot learning with code
- learn meta-learning from scratch
- find datasets for one-shot image recognition
- discover meta-reinforcement learning resources
- find lecture videos on meta-learning
- locate libraries for prototypical networks

## When to choose
- starting research or study in meta-learning
- looking for implementations of classic few-shot learning papers
- compiling a reading list on zero/one/few-shot learning

## When to avoid
- need a maintained production library rather than a resource list
- need up-to-date 2021+ papers since the list was last updated in 2020

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, reinforcement-learning
- domain: machine-learning, deep-learning, artificial-intelligence, awesome-lists, tutorials
- platform: cross-platform
- tags: meta-learning, few-shot-learning, one-shot-learning, zero-shot-learning, curated-list, papers, datasets

## Member repositories
- sudharsan13296/Awesome-Meta-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.996599+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 2026-08-30T04:30:06.867496+00:00, confidence not recorded.
  - readme: https://github.com/sudharsan13296/Awesome-Meta-Learning (fetched 2026-08-28T04:05:02.996599+00:00, sha 1af3ea7df4db)
- Data as of 2026-08-30T08:39:29.467469+00:00.