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Learn2Learn is a PyTorch-based library focused on meta-learning and few-shot learning research. It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization.
Features
- Modular and extensible PyTorch library for meta-learning
- Includes implementations of state-of-the-art meta-learning algorithms
- Supports few-shot learning, MAML, and optimization-based meta-learning
- Provides benchmarks and datasets for rapid experimentation
- Easy integration with existing PyTorch models and training loops
- Well-documented and maintained with active research community use
Project Samples
License
MIT LicenseFollow learn2learn
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