  Style transfer is a significant problem of machine learning with numerous
successful applications. In this work, we present a novel style transfer
framework building upon infinite task learning and vector-valued reproducing
kernel Hilbert spaces. We instantiate the idea in emotion transfer where the
goal is to transform facial images to different target emotions. The proposed
approach provides a principled way to gain explicit control over the continuous
style space. We demonstrate the efficiency of the technique on popular facial
emotion benchmarks, achieving low reconstruction cost and high emotion
classification accuracy.
