cbfinn/maml

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2727 stars · 621 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementation of Model-Agnostic Meta-Learning (MAML) from Finn et al., ICML 2017, for fast adaptation of deep networks. It includes few-shot supervised learning experiments on sinusoid regression, Omniglot classification, and MiniImagenet classification using TensorFlow.

Use cases

• implement few-shot learning with MAML
• reproduce MAML paper experiments
• learn meta-learning algorithms
• run few-shot classification on Omniglot or MiniImagenet
• adapt a deep network to new tasks with few examples

When to choose

• you want the canonical reference implementation of MAML for supervised few-shot learning
• you are doing research on meta-learning and need a baseline
• you want to reproduce the ICML 2017 paper results

When to avoid

• you need MAML for reinforcement learning (use the separate maml_rl codebase)
• you need a maintained production framework or modern PyTorch support
• you need up-to-date TensorFlow compatibility

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python meta-learning few-shot-learning tensorflow research-code maml algorithms

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markdown · JSON · MCP: product_card(name="cbfinn/maml")