Domain generation via learned partial domain translations

A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to:

• receive, at a trained deep neural network, a first image having a first label representing a perspective of the first image and a first context representing an environment of the first image;
• and
• generate, via a plurality of mapping of the trained deep neural network, a second image having the first label and a second context representing an environment of the second image, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label.

A method comprising:

• receiving, at a trained deep neural network, a first dataset having a first label and a first context;
• and
• generating, at the trained deep neural network, a second dataset having the first label and a second context according to a plurality of mapping, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label.