Abstract: Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.

Abstract: Techniques are generally described for generation of photorealistic synthetic image data. A generator network generates first synthetic image data. A first class of image data represented by a first portion of the first synthetic image data is detected and the first portion is sent to a first discriminator network. The first discriminator network generates a prediction of whether the first portion of the first synthetic image data is synthetically generated. A second class of image data represented by a second portion of the first synthetic image data is detected and the second portion is sent to a second discriminator network. The second discriminator network generates a prediction of whether the second portion of the first synthetic image data is synthetically generated. The generator network is updated based on the predictions of the discriminators.