# Elastic Weight Consolidation (EWC): Nuts and Bolts

arXiv: https://arxiv.org/abs/2105.04093 · HTML: https://mlsanity.com/paper/2105.04093/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 6 of 6)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Localizing Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1906.02568/) (2019)

## Built on by (most cited first, 23 of 23)

- [Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening](https://mlsanity.com/paper/2308.07707/) (2023)
- [On the Computation of the Fisher Information in Continual Learning](https://mlsanity.com/paper/2502.11756/) (2025)
- [CORAL: Scalable Multi-Task Robot Learning via LoRA Experts](https://mlsanity.com/paper/2603.09298/) (2026)
- [CRL-VLA: Continual Vision-Language-Action Learning](https://mlsanity.com/paper/2602.03445/) (2026)
- [PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning](https://mlsanity.com/paper/2601.13020/) (2026)
- [CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation](https://mlsanity.com/paper/2401.08940/) (2024)
- [Provably Safe Model Updates](https://mlsanity.com/paper/2512.01899/) (2025)
- [SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators](https://mlsanity.com/paper/2603.20410/) (2026)
- [CLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity Promotion](https://mlsanity.com/paper/2405.09637/) (2024)
- [MLLM-CTBench: A Benchmark for Continual Instruction Tuning with Reasoning Process Diagnosis](https://mlsanity.com/paper/2508.08275/) (2025)
- [Sequential Learning in the Dense Associative Memory](https://mlsanity.com/paper/2409.15729/) (2024)
- [SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models](https://mlsanity.com/paper/2502.18168/) (2025)
- [A Study on Regularization-Based Continual Learning Methods for Indic ASR](https://mlsanity.com/paper/2508.06280/) (2025)
- [The Role of Bio-Inspired Modularity in General Learning](https://mlsanity.com/paper/2109.15097/) (2021)
- [GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes](https://mlsanity.com/paper/2212.00572/) (2022)
- [The Effect of Masking Strategies on Knowledge Retention by Language Models](https://mlsanity.com/paper/2306.07185/) (2023)
- [Multi-Modal Parameter-Efficient Fine-tuning via Graph Neural Network](https://mlsanity.com/paper/2408.00290/) (2024)
- [Low-Rank Filtering and Smoothing for Sequential Deep Learning](https://mlsanity.com/paper/2410.06800/) (2024)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)
- [Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer](https://mlsanity.com/paper/2601.22274/) (2026)
- [oslash Source Models Leak What They Shouldn't nrightarrow: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization](https://mlsanity.com/paper/2604.08238/) (2026)
- [Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models](https://mlsanity.com/paper/2607.20493/) (2026)
- [PoCoFL: POlicy-COmpliant Federated Learning](https://mlsanity.com/paper/2610.03650/) (2026)

## Abstract (arXiv)

In this report, we present a theoretical support of the continual learning method 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.