Continual Learning

Field

Artificial Intelligence, Machine Learning

Definition

A set of approaches to train machine learning models incrementally, using data samples only once as they arrive, from a continuous stream of information.

Importance

Helps models adapt to changing environments, improving robustness and reducing catastrophic forgetting.

Role in AGI

Fundamental breakthrough required for Artificial General Intelligence (AGI).

Primary Goal

Enable autonomous, incremental development of ever more complex skills and knowledge without catastrophic forgetting or interference.

Also Known As

Lifelong learning, online machine learning

Methods Categories

Regularization-based, architectural, and memory-based

Key Challenge Addressed

Catastrophic Forgetting

Undated (current discussion)

Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks. Adapting continual learning is an incremental process, from carefully identifying the objective over implementing a simple baseline solution to selecting and tuning the continual learning method. [...] Continual learning is a fascinating concept that can help you train effective ML models incrementally. Training incrementally is crucial when a model needs to adapt to new data or be personalized. [...] These problems keep many ML practitioners awake at night. If you’re part of this group, continual learning is exactly what you need. ## What is continual learning? Continual learning (CL) is a research field focusing on developing practical approaches for effectively training machine learning models incrementally.

Continual Learning, also known as Lifelong learning, is built on the idea of learning continuously about the external world in order to enable the autonomous, incremental development of ever more complex skills and knowledge. [...] A Continual learning system can be defined as an adaptive algorithm capable of learning from a continuous stream of information, with such information becoming progressively available over time and where the number of tasks to be learned (e.g. membership classes in a classification task) are not predefined. Critically, the accommodation of new information should occur without catastrophic forgetting or interference. [...] Even if Catastrophic Forgetting is the main focus of Continual Learning, there are other aspects that need to be considered when learning continuously. Preserving old knowledge is important not only to perform well on previous tasks. It can also be used to perform better on incoming tasks. This feature, called transfer learning, enables Continual Learning algorithms to require only few examples of a new tasks to master it.

With advances in artificial intelligence (AI), particularly in machine learning and deep learning, the potential uses for AI in medicine are growing. Continual learning, also known as lifelong learning or online machine learning, is a fundamental idea in machine learning in which models continuously learn and evolve based on the input of increasing amounts of data, while retaining previously learned knowledge.1 1. Parisi, GI ∙ Kemker, R ∙ Part, JL ∙ et al.

Continual Learning refers to the ability to learn from non-stationary information streams incrementally. “Non-stationary” represents continuously changing data distributions. “Incremental” learning refers to preserving previous knowledge while continuously learning new information. [...] Continual learning enables AI systems to consistently update and expand knowledge in rapidly changing environments. In machine learning, continual learning addresses catastrophic forgetting by allowing models to integrate new information over time without losing previously acquired knowledge, resulting in more adaptive and robust systems. [...] Continual learning enables AI systems to integrate new knowledge over time without forgetting previous information. Why is continual learning important? It helps models adapt to changing environments, improving robustness and reducing "catastrophic forgetting". How is continual learning applied in industry? Applications span from online customer modeling to adaptive threat detection, where models update incrementally with fresh data. Open All Close All