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We explore the behavior of a standard convolutional neural net in acontinual-learning setting that introduces visual classification taskssequentially and requires the net to master new tasks while preserving masteryof previously learned tasks. This setting corresponds to that which humanlearners face as they acquire domain expertise serially, for example, as anindividual studies a textbook. Through simulations involving sequences of tenrelated visual tasks, we find reason for optimism that nets will scale well asthey advance from having a single skill to becoming multi-skill domain experts.We observe two key phenomena. First, \emph{forward facilitation}---theaccelerated learning of task $n+1$ having learned $n$ previous tasks---growswith $n$. Second, \emph{backward interference}---the forgetting of the $n$previous tasks when learning task $n+1$---diminishes with $n$. Amplifyingforward facilitation is the goal of research on metalearning, and attenuatingbackward interference is the goal of research on catastrophic forgetting. Wefind that both of these goals are attained simply through broader exposure to adomain.

Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget