# Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks

arXiv: https://arxiv.org/abs/2106.05872 · HTML: https://mlsanity.com/paper/2106.05872/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Streaming Variational Bayes](https://mlsanity.com/paper/1307.6769/) (2013)
- [FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices](https://mlsanity.com/paper/1809.06970/) (2018)
- [DSP.Ear: Leveraging Co-Processor Support for Continuous Audio Sensing on Smartphones](https://mlsanity.com/paper/1409.3206/) (2014)

## Built on by (most cited first, 7 of 7)

- [Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications](https://mlsanity.com/paper/2110.13290/) (2021)
- [LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms](https://mlsanity.com/paper/2311.11420/) (2023)
- [FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications](https://mlsanity.com/paper/2106.07268/) (2021)
- [IBCL: Zero-shot Model Generation under Stability-Plasticity Trade-offs](https://mlsanity.com/paper/2305.14782/) (2023)
- [Enabling On-Device Smartphone GPU based Training: Lessons Learned](https://mlsanity.com/paper/2202.10100/) (2022)
- [YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers](https://mlsanity.com/paper/2203.03794/) (2022)
- [MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices](https://mlsanity.com/paper/2504.00174/) (2025)

## Abstract (arXiv)

Despite much research targeted at enabling conventional machine learning models to continually learn tasks and data distributions sequentially without forgetting the knowledge acquired, little effort has been devoted to account for more realistic situations where learning some tasks accurately might be more critical than forgetting previous ones. In this paper we propose a Bayesian inference based framework to continually learn a set of real-world, sensing-based analysis tasks that can be tuned to prioritize the remembering of previously learned tasks or the learning of new ones. Our experiments prove the robustness and reliability of the learned models to adapt to the changing sensing environment, and show the suitability of using uncertainty of the predictions to assess their reliability.