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Elastic Weight Consolidation (EWC): Nuts and Bolts

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arxiv 2105.04093 v1 pith:TE54NFHF submitted 2021-05-10 cs.CV cs.LGstat.ML

Abhishek Aich This is my paper · ORCID
classification cs.CVcs.LGstat.ML
keywords continuallearningconsolidationelasticreportweightassumeaware
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In this report, we present a theoretical support of the continual learning method \textbf{Elastic Weight Consolidation}, introduced in paper titled `Overcoming catastrophic forgetting in neural networks'. Being one of the most cited paper in regularized methods for continual learning, this report disentangles the underlying concept of the proposed objective function. We assume that the reader is aware of the basic terminologies of continual learning.

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Cited by 1 Pith paper

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  1. Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer

    cs.LG 2026-01 reject novelty 6.0 of 10

    A server-side proximal anchor on FedAvg provably limits forgetting and gives a task-uniform convergence rate of O(sqrt(E/(NT))) for federated domain-incremental learning under partial participation.

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