Continual learning is a fundamental concept in the field of machine learning and artificial intelligence, referring to the ability of a learning system to continuously adapt and improve its performance over time by acquiring new knowledge and skills without forgetting previously learned information. Unlike traditional machine learning paradigms, where the learning process is typically divided into distinct training and testing phases, continual learning aims to enable models to learn incrementally from a stream of data, mimicking the way humans learn throughout their lives. This approach involves addressing key challenges such as catastrophic forgetting, where the model's performance on previously learned tasks degrades as it learns new tasks, and the ability to transfer knowledge across related tasks. Continual learning systems employ various techniques, including regularization methods, dynamic architectures, and memory-based approaches, to mitigate forgetting and facilitate the integration of new knowledge. The development of effective continual learning algorithms has significant implications for a wide range of applications, from autonomous systems that need to adapt to changing environments to personalized assistants that can learn and grow with their users over time.
lifelong learning, incremental learning, adaptive models, knowledge transfer, catastrophic forgetting, dynamic architectures
CITATION : "Robert Anderson. 'Continual Learning.' Design+Encyclopedia. https://design-encyclopedia.com/?E=431613 (Accessed on October 08, 2024)"
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