mlsanityData to 14 Oct 2026

Evaluation of Regularization-based Continual Learning Approaches: Application to HAR

arXiv 2304.13327 ↗First posted 26 Apr 2023cs.AI stat.MLcited by 3

Not on the map. Why?

Abstract (arXiv)

Pervasive computing allows the provision of services in many important areas, including the relevant and dynamic field of health and well-being. In this domain, Human Activity Recognition (HAR) has gained a lot of attention in recent years. Current solutions rely on Machine Learning (ML) models and achieve impressive results. However, the evolution of these models remains difficult, as long as a complete retraining is not performed. To overcome this problem, the concept of Continual Learning is very promising today and, more particularly, the techniques based on regularization. These techniques are particularly interesting for their simplicity and their low cost. Initial studies have been conducted and have shown promising outcomes. However, they remain very specific and difficult to compare. In this paper, we provide a comprehensive comparison of three regularization-based methods that we adapted to the HAR domain, highlighting their strengths and limitations. Our experiments were conducted on the UCI HAR dataset and the results showed that no single technique outperformed all others in all scenarios considered.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 22 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

11 of 22 references.

References · 11

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  2. 2016Learning without Forgettingcited by 2,496
  3. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  4. 2017Gradient Episodic Memory for Continual Learningcited by 1,898
  5. 2017Continual Learning with Deep Generative Replaycited by 1,158
  6. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 449
  7. 2017Measuring Catastrophic Forgetting in Neural Networkscited by 360
  8. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  9. 2022Reinforced Continual Learning for Graphscited by 24
  10. 2021Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysiscited by 21
  11. 2018Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networkscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classificationcited by 1
  2. 2025TaskVAE: Task-Specific Variational Autoencoders for Exemplar Generation in Continual Learning for Human Activity Recognitioncited by 0
  3. 2026CLeAN: Continual Learning Adaptive Normalization in Dynamic Environmentscited by 0