  In recent years, great strides have been made in the field of affective
computing. Several models have been developed to represent and quantify
emotions. Two popular ones include (i) categorical models which represent
emotions as discrete labels, and (ii) dimensional models which represent
emotions in a Valence-Arousal (VA) circumplex domain. However, there is no
standard for annotation mapping between the two labelling methods. We build a
novel algorithm for mapping categorical and dimensional model labels using
annotation transfer across affective facial image datasets. Further, we utilize
the transferred annotations to learn rich and interpretable data
representations using a variational autoencoder (VAE). We present "LeVAsa", a
VAE model that learns implicit structure by aligning the latent space with the
VA space. We evaluate the efficacy of LeVAsa by comparing performance with the
Vanilla VAE using quantitative and qualitative analysis on two benchmark
affective image datasets. Our results reveal that LeVAsa achieves high
latent-circumplex alignment which leads to improved downstream categorical
emotion prediction. The work also demonstrates the trade-off between degree of
alignment and quality of reconstructions.
