About the CL Landscape

Continual Learning (also known as Lifelong Learning or Incremental Learning) addresses one of the fundamental challenges in machine learning: catastrophic forgetting.

When neural networks are trained on a sequence of tasks, they tend to abruptly forget previously learned knowledge as they adapt to new data. This stands in stark contrast to human learning, where we continuously accumulate knowledge throughout our lives.

Continual learning strategies aim to bridge this gap by developing methods that allow models to learn from a stream of data while retaining knowledge from the past — balancing the fundamental tension between stability (retaining old knowledge) and plasticity (learning new information).

We welcome contributions from the research community! To add a new strategy to the landscape:

1. Fork the repository on GitHub.
2. Add your strategy entry to /data/strategies.json following the existing format.
3. Include the strategy name, category, year, description, arXiv link, and relevant tags.
4. Open a Pull Request with a brief description of the strategy.

This project would not be possible without the incredible work of the continual learning research community. Special thanks to:

• •ContinualAI — For building and nurturing the largest CL research community.
• •CNCF Landscape — For providing the inspiration for this visual mapping approach.
• •All researchers — Whose papers and implementations make this landscape possible.