Style-based architecture for generative neural networks

A system, comprising:

• a mapping neural network that is configured to:
• process a latent code defined in an input space to produce an intermediate latent code defined in an intermediate latent space;
• and
• convert the intermediate latent code into a first style signal;
• and
• a synthesis neural network that is configured to:
• apply the first style signal at a first layer to modify first intermediate data according to the first style signal to produce modified first intermediate data;
• process the modified first intermediate data to produce second intermediate data;
• apply a second style signal at a second layer of the synthesis neural network to modify the second intermediate data according to the second style signal to produce modified second intermediate data;
• and
• process the modified second intermediate data to produce output data including content corresponding to the second intermediate data.

A computer-implemented method, comprising:

• applying a first set of spatial noise at a first layer of a synthesis neural network to generate modified first intermediate data comprising content corresponding to first intermediate data that is modified based on the first set of spatial noise;
• processing the modified first intermediate data to produce second intermediate data;
• applying a second set of spatial noise at a second layer of the synthesis neural network to generate modified second intermediate data comprising content corresponding to the second intermediate data that is modified based on the second set of spatial noise;
• and
• processing the modified second intermediate data to produce output data comprising content corresponding to the modified second intermediate data.