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Generative adversarial networks offer the possibility to generate deceptivelyreal images that are almost indistinguishable from actual photographs. Suchsystems however rely on the presence of large datasets to realisticallyreplicate the corresponding domain. This is especially a problem if not onlyrandom new images are to be generated, but specific (continuous) features areto be co-modeled. A particularly important use case in \emph{Human-ComputerInteraction} (HCI) research is the generation of emotional images of humanfaces, which can be used for various use cases, such as the automaticgeneration of avatars. The problem hereby lies in the availability of trainingdata. Most suitable datasets for this task rely on categorical emotion modelsand therefore feature only discrete annotation labels. This greatly hinders thelearning and modeling of smooth transitions between displayed affective states.To overcome this challenge, we explore the potential of label interpolation toenhance networks trained on categorical datasets with the ability to generateimages conditioned on continuous features.

Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks