Image Transformation with a Hybrid Autoencoder and Generative Adversarial Network Machine Learning Architecture

A computer-implemented method comprising:

• obtaining, from a memory, an input image patch;
• applying, by a processor, an encoder artificial neural network (ANN) to the input image patch, wherein the encoder ANN is configured to produce a feature vector from the input image patch, wherein the encoder ANN has been trained with a first plurality of domain training images such that an output image patch visually resembling the input image patch can be generated from the feature vector;
• applying, by the processor, a generator ANN to the feature vector, wherein the generator ANN is configured to produce a generated image patch from the feature vector, wherein the generator ANN has been trained with feature vectors derived from the first plurality of domain training images and a second plurality of generative training images such that the generated image patch visually resembles the input image patch but is constructed of a newly-generated image elements visually resembling one or more image patches from the second plurality of generative training images;
• and
• storing, in the memory, the generated image patch.

A system comprising:

• a first plurality of domain training images;
• a second plurality of generative training images;
• an autoencoder including:
• an encoder artificial neural network (ANN) configured to receive an input image patch from an image of the first plurality of domain training images and produce a first feature vector therefrom, and a decoder ANN configured to receive the first feature vector and produce an output image patch therefrom, wherein the encoder ANN and the decoder ANN are trained based on a first loss function that calculates a first difference between the input image patch and the output image patch;
• a generative adversarial network including:
• a generator ANN configured to receive the first feature vector and produce a generated image patch from the first feature vector, and a discriminator ANN configured to receive the generated image patch and a particular generative training image of the second plurality of generative training images, and provide classifications thereof predicting whether the generated image patch belongs to the second plurality of generative training images, wherein the discriminator ANN is trained based on a second loss function that calculates a second difference between a classification of the generated image patch and a classification of the particular generative training image;
• and
• wherein the encoder ANN is also configured to receive the generated image patch and produce a second feature vector therefrom, and wherein the generator ANN is trained based on a third loss function that calculates a third difference between (i) the classification of the generated image patch and (ii) a fourth difference between the first feature vector and the second feature vector.