Gradient Episodic Memory for Continual Learning
David Lopez-Paz · Marc'Aurelio Ranzato

Mon Dec 4th 06:30 -- 10:30 PM @ Pacific Ballroom #4 #None

One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we study the problem of continual learning, where the model observes, 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 data. These metrics characterize models not only by their test accuracy, but also in terms of their ability to transfer knowledge across tasks. Second, we propose a model for continual learning, called Gradient Episodic Memory (GEM) that alleviates forgetting, while allowing beneficial transfer of knowledge to previous tasks. Our experiments on variants of the MNIST and CIFAR-100 datasets demonstrate the strong performance of GEM when compared to the state-of-the-art.

Author Information

David Lopez-Paz (Facebook AI Research)
Marc'Aurelio Ranzato (Facebook)

Marc'Aurelio Ranzato is a research scientist and manager at the Facebook AI Research lab in New York City. His research interests are in the area of unsupervised learning, continual learning and transfer learning, with applications to vision, natural language understanding and speech recognition. Marc'Aurelio has earned a PhD in Computer Science at New York University under Yann LeCun's supervision. After a post-doc with Geoffrey Hinton at University of Toronto, he joined the Google Brain team in 2011. In 2013 he joined Facebook and was a founding member of the Facebook AI Research lab.

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