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Meta-Consolidation for Continual Learning
Joseph K J · Vineeth N Balasubramanian

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1193

The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep learning systems fall short of. In this work, we present a novel methodology for continual learning called MERLIN: Meta-Consolidation for Continual Learning.

We assume that weights of a neural network, for solving task, come from a meta-distribution. This meta-distribution is learned and consolidated incrementally. We operate in the challenging online continual learning setting, where a data point is seen by the model only once.

Our experiments with continual learning benchmarks of MNIST, CIFAR-10, CIFAR-100 and Mini-ImageNet datasets show consistent improvement over five baselines, including a recent state-of-the-art, corroborating the promise of MERLIN.

Author Information

Joseph K J (Indian Institute of Technology Hyderabad)
Vineeth N Balasubramanian (Indian Institute of Technology, Hyderabad)

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