Reference 8 stops to get here

Continual Learning

Learning new tasks sequentially without forgetting previously learned tasks, addressing catastrophic forgetting.

Your route here

8 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Unsupervised Learning ✓ understood

    Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

  5. Self-Supervised Learning ✓ understood

    Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).

  6. Pre-training ✓ understood

    Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

  7. Fine-Tuning ✓ understood

    The process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain.

  8. Catastrophic Forgetting ✓ understood

    The tendency of neural networks to completely forget previously learned information when learning new tasks.

  9. Continual Learning · you are here ✓ understood

Where it sits

Before this

Catastrophic Forgetting
Continual Learning

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training Online Learning Models that learn continuously from streaming data, updating incrementally as new data arrives. Training Transfer Learning Leveraging knowledge learned from one task/domain to improve performance on a related task with less data. Training Meta-Learning Learning to learn - training models that can quickly adapt to new tasks with minimal data, often applied to few-shot learning scenarios. Shipping AI Model Retraining Periodically updating models with new data to maintain performance as distributions change. Training Multi-Task Learning Training a single model on multiple related tasks simultaneously to improve generalization and efficiency.

In the research

All papers →

2 papers that build on Continual Learning .