Transcription of Gradient Episodic Memory for Continual Learning
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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.
In this section, we propose Gradient Episodic Memory (GEM), a model for continual learning, as introduced in Section 2. The main feature of GEM is an episodic memory M t, which stores a subset of the observed examples from task t. For simplicity, we assume integer task descriptors, and use them to index the episodic memory.
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