Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders

Understand

Selective manipulation of data attributes using deep generative models is an active area of research.

• In this paper, we present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly.
• This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded.
• Consequently, post-training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension.