Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization

Understand

Given the growing trend of continual learning techniques for deep neural networks focusing on the domain of computer vision, there is a need to identify which of these generalizes well to other tasks such as human activity recognition (HAR).

• As recent methods have mostly been composed of loss regularization terms and memory replay, we provide a constituent-wise analysis of some prominent task-incremental learning techniques employing these on HAR datasets.
• We find that most regularization approaches lack substantial effect and provide an intuition of when they fail.
• Thus, we make the case that the development of continual learning algorithms should be motivated by rather diverse task domains.