Stratified Transfer Learning for Cross-domain Activity Recognition

TL;DR

This paper introduces Stratified Transfer Learning (STL), a novel framework that leverages intra-class affinity to improve cross-domain activity recognition accuracy, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a new intra-class transfer learning framework, STL, which enhances cross-domain activity recognition by exploiting class intra-affinity and iterative pseudo-labeling.

Findings

STL achieves a 7.68% accuracy improvement over state-of-the-art methods.

It effectively handles different degrees of domain similarity and activity levels.

Experimental results validate STL's superior performance on three large datasets.

Abstract

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.