It's LeVAsa not LevioSA! Latent Encodings for Valence-Arousal Structure Alignment
This work addresses a domain-specific problem in affective computing for researchers and practitioners by providing a novel method for annotation mapping, though it is incremental as it builds on existing VAE techniques.
The paper tackles the lack of a standard for mapping between categorical and dimensional emotion labels in affective computing by developing LeVAsa, a VAE model that aligns latent space with Valence-Arousal space, resulting in high latent-circumplex alignment and improved downstream emotion prediction.
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.