mlsanityData to 7 Oct 2026

EVCL: Elastic Variational Continual Learning with Weight Consolidation

arXiv 2406.15972 ↗First posted 23 Jun 2024cs.LG stat.MLcited by 9

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Abstract (arXiv)

Continual learning aims to allow models to learn new tasks without forgetting what has been learned before. This work introduces Elastic Variational Continual Learning with Weight Consolidation (EVCL), a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learning (VCL) with the regularization-based parameter-protection strategy of Elastic Weight Consolidation (EWC). By combining the strengths of both methods, EVCL effectively mitigates catastrophic forgetting and enables better capture of dependencies between model parameters and task-specific data. Evaluated on five discriminative tasks, EVCL consistently outperforms existing baselines in both domain-incremental and task-incremental learning scenarios for deep discriminative models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 23 Jun 2024.

Builds on

23 of 37 references.

References · 23

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Progressive Neural Networkscited by 1,332
  4. 2017Continual Learning with Deep Generative Replaycited by 1,152
  5. 2018Experience Replay for Continual Learningcited by 1,074
  6. 2015Optimizing Neural Networks with Kronecker-factored Approximate Curvaturecited by 776
  7. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 711
  8. 2018Progress & Compress: A scalable framework for continual learningcited by 516
  9. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  10. 2017Variational Continual Learningcited by 335
  11. 2017Overcoming Catastrophic Forgetting by Incremental Moment Matchingcited by 243
  12. 2019Practical Deep Learning with Bayesian Principlescited by 158
11 more
  1. 2023The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reductioncited by 120
  2. 2018Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilizationcited by 104
  3. 2020Continual Deep Learning by Functional Regularisation of Memorable Pastcited by 104
  4. 2017Noisy Natural Gradient as Variational Inferencecited by 94
  5. 2019Toward Understanding Catastrophic Forgetting in Continual Learningcited by 67
  6. 2020Generalized Variational Continual Learningcited by 56
  7. 2017On Quadratic Penalties in Elastic Weight Consolidationcited by 54
  8. 2017Online Natural Gradient as a Kalman Filtercited by 25
  9. 2022BayesFormer: Transformer with Uncertainty Estimationcited by 19
  10. 2022Sparse Coding in a Dual Memory System for Lifelong Learningcited by 16
  11. 2019Improving and Understanding Variational Continual Learningcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2026Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systemscited by 7
  2. 2025Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysiscited by 2
  3. 2025Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanismcited by 1
  4. 2025Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Exercise Recommendationcited by 0
  5. 2025ONG: Orthogonal Natural Gradient Descentcited by 0
  6. 2026Finding Structure in Continual Learningcited by 0
  7. 2026AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detectioncited by 0
  8. 2026Task Switching Without Forgetting via Proximal Decouplingcited by 0
  9. 2026Fast and Slow Variational Continual Learningcited by 0