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Gradient Episodic Memory for Continual Learning

Gradient Episodic Memory for Continual LearningDavid Lopez-Paz and Marc Aurelio RanzatoFacebook Artificial Intelligence major obstacle towards AI is the poor ability of models to solve new prob-lems quicker, and without forgetting previously acquired knowledge. To betterunderstand this issue, we study the problem ofcontinual Learning , where the modelobserves, once and one by one, examples concerning a sequence of tasks. First,we propose a set of metrics to evaluate models Learning over a continuum of metrics characterize models not only by their test accuracy, but also in termsof their ability to transfer knowledge across tasks.

structured objects, such as a paragraph of natural language explaining how to solve the i-th task. Rich task descriptors offer an opportunity for zero-shot learning, since the relation between tasks could be inferred using new task descriptors alone. Furthermore, task descriptors disambiguate similar learning tasks. In particular, the same input x

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