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- W3102605217 abstract "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 $boldsymbol psi$, for solving task $boldsymbol t$, come from a meta-distribution $p(boldsymbol{psi|t})$. 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." @default.
- W3102605217 created "2020-11-23" @default.
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- W3102605217 date "2020-12-01" @default.
- W3102605217 modified "2023-09-24" @default.
- W3102605217 title "Meta-Consolidation for Continual Learning" @default.
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