Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels

Abstract

The prevalence of mobile sensors makes it possible for researchers to collect and analyze the pervasive sensed human activity data with machine learning tools. These analyses and applications heavily rely on the self-report survey data that reflect human physical and psychological behaviors. However, many factors can affect the reliability of such self-report surveys, such as participants’ trusty and ambiguous descriptions in questionnaires. These inaccurate survey labels challenge the development of the models, especially when it comes to the case where deep learning approaches tend to memorize the noisy information from training data. In this paper, we set out to deal with the noisy self-report survey labels. We aim to address the following challenges: 1)the limited access to clean labeled data in real-world scenarios; 2)different noise ratio of the data from different participants; 3)various types of noise that appear in the data; 4)how to evaluate the reliability of participants. As such, we propose a generalizable model, Lan, for learning from multi-modal sensory data and noisy labels. The proposed model incorporates the temporal information from sensory data to guide the model training and thus to augment noise tolerance. Extensive experiments on a real-world dataset were conducted to show our model outperforms state-of-the-art benchmarks.