# Meta-Consolidation for Continual Learning Source: [Meta-Consolidation for Continual Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:37:00.137Z Authors: K J Joseph, Vineeth N Balasubramanian Published: 2020-10-01T12:34:35.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()