AffRankNet+: Ranking Affect Using Privileged Information

TL;DR

This paper introduces AffRankNet+, a neural network ranking model that leverages privileged information during training to improve affect state ranking accuracy, marking the first such application in this context.

Contribution

It extends RankNet to incorporate privileged information in the LUPI paradigm for affect modeling, demonstrating significant performance improvements.

Findings

AffRankNet+ outperforms RankNet on Afew-VA dataset.

Privileged information enhances affect ranking accuracy.

First neural network ranking model to exploit privileged information.

Abstract

Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which is not available at test time. Learning under this setting is called Learning Under Privileged Information (LUPI). At the same time, due to the ordinal nature of affect annotations, formulating affect modelling tasks as supervised learning ranking problems is gaining ground within the Affective Computing research community. Motivated by the two facts above, in this study, we introduce a ranking model that treats additional information about the training data as privileged information to accurately rank affect states. Our ranking model extends the well-known RankNet model to the LUPI paradigm, hence its name AffRankNet+. To the best of our knowledge, it is the first time that a ranking model based on neural networks exploits privileged information. We evaluate the performance of the proposed model on the public available Afew-VA dataset and compare it against the RankNet model, which does not use privileged information. Experimental evaluation indicates that the AffRankNet+ model can yield significantly better performance.