Modelling Sample Informativeness for Deep Affective Computing

Abstract

Using data with high quality annotation is crucial in emotion recognition applications, especially because the task is subjective and the raters may exhibit disagreement with respect to each sample. In this paper, we propose a meta-learning methodology that can reason about the training data and detect potentially less informative instances in order to reduce their impact in the training process. The way we inform the meta-learner on the importance of each sample is by utilising recent advances in uncertainty modelling with Bayesian neural networks that can decompose predictive uncertainty into: a) model uncertainty that is due to a lack of observations and b) label uncertainty that is due to inherent randomness in the data labelling, which we adapt for affective computing. Our proposed method for soft data selection exhibits a 6% absolute improvement in Concordance Correlation Coefficient with respect to the baseline in a two-dimensional continuous affect recognition task.