Generative domain adaptation in a neural network

A system, comprising a computer including a processor and a memory, the memory storing instructions executable by the processor to cause the processor to:

• generate a low-level representation of the input source domain data by processing source domain data using a source domain low-level encoder neural network layer corresponding to data from the source domain to generate a low-level representation of the input source domain data;
• generate an embedding of the input source domain data by processing the low-level representation using a high-level encoder neural network layer shared between data from the source and target domains;
• generate a high-level feature representation of features of the input source domain data by processing the embedding of the input source domain image using a high-level decoder neural network layer shared between data from the source and target domains to generate a high-level feature representation of features of the input source domain data;
• generate output target domain data in the target domain that includes semantics corresponding to the input source domain data by processing the high-level feature representation of the features of the input source domain data using a domain low-level decoder neural network layer that generate data from the target;
• and
• modify a loss function such that latent attributes corresponding to the embedding are selected from a same probability distribution.