Title: Latent Domain Transfer: Crossing modalities with Bridging Autoencoders. Abstract: Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. 
However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video), 
utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes. 
Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. 
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network). 
We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. 
The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions. 