Workshop on Continual Learning

Haytham Fayek · Arslan Chaudhry · David Lopez-Paz · Eugene Belilovsky · Jonathan Schwarz · Marc Pickett · Rahaf Aljundi · Sayna Ebrahimi · Razvan Pascanu · Puneet Dokania

Keywords:  continual learning    catastrophic forgetting    Knowledge transfer  

Machine learning systems are commonly applied to isolated tasks or narrow domains (e.g. control over similar robotic bodies). It is further assumed that the learning system has simultaneous access to all the data points of the tasks at hand. In contrast, Continual Learning (CL) studies the problem of learning from a stream of data from changing domains, each connected to a different learning task. The objective of CL is to quickly adapt to new situations or tasks by exploiting previously acquired knowledge, while protecting previous learning from being erased. Meeting the objectives of CL will provide an opportunity for systems to quickly learn new skills given knowledge accumulated in the past and continually extend their capabilities to changing environments, a hallmark of natural intelligence.

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