  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.
