# Bayesian Structure Adaptation for Continual Learning

arXiv: https://arxiv.org/abs/1912.03624 · HTML: https://mlsanity.com/paper/1912.03624/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning](https://mlsanity.com/paper/2307.09218/) (2023)
- [Recent Advances of Continual Learning in Computer Vision: An Overview](https://mlsanity.com/paper/2109.11369/) (2021)
- [How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition](https://mlsanity.com/paper/2207.07730/) (2022)
- [Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans](https://mlsanity.com/paper/2302.00162/) (2023)
- [On Sequential Bayesian Inference for Continual Learning](https://mlsanity.com/paper/2301.01828/) (2023)
- [Restricted Orthogonal Gradient Projection for Continual Learning](https://mlsanity.com/paper/2301.12131/) (2023)
- [Continual Learning From a Stream of APIs](https://mlsanity.com/paper/2309.00023/) (2023)
- [Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks](https://mlsanity.com/paper/2307.07753/) (2023)
- [HyperMask: Adaptive Hypernetwork-based Masks for Continual Learning](https://mlsanity.com/paper/2310.00113/) (2023)
- [CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks](https://mlsanity.com/paper/2410.14749/) (2024)
- [Bayesian Transfer Learning](https://mlsanity.com/paper/2312.13484/) (2023)
- [Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition](https://mlsanity.com/paper/2401.06287/) (2024)
- [Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network](https://mlsanity.com/paper/2202.13369/) (2022)
- [Temporal-Difference Variational Continual Learning](https://mlsanity.com/paper/2410.07812/) (2024)
- [The Bayesian Approach to Continual Learning: An Overview](https://mlsanity.com/paper/2507.08922/) (2025)
- [Generative Kernel Continual learning](https://mlsanity.com/paper/2112.13410/) (2021)
- [Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning](https://mlsanity.com/paper/2207.14202/) (2022)
- [Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems](https://mlsanity.com/paper/2507.21589/) (2025)
- [Finding Structure in Continual Learning](https://mlsanity.com/paper/2602.04555/) (2026)
- [Task Switching Without Forgetting via Proximal Decoupling](https://mlsanity.com/paper/2604.18857/) (2026)

## Abstract (arXiv)

Continual Learning is a learning paradigm where learning systems are trained with sequential or streaming tasks. Two notable directions among the recent advances in continual learning with neural networks are (i) variational Bayes based regularization by learning priors from previous tasks, and, (ii) learning the structure of deep networks to adapt to new tasks. So far, these two approaches have been orthogonal. We present a novel Bayesian approach to continual learning based on learning the structure of deep neural networks, addressing the shortcomings of both these approaches. The proposed model learns the deep structure for each task by learning which weights to be used, and supports inter-task transfer through the overlapping of different sparse subsets of weights learned by different tasks. Experimental results on supervised and unsupervised benchmarks shows that our model performs comparably or better than recent advances in continual learning setting.