Adapting a generative adversarial network to new data sources for image classification

A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement a generative adversarial network (GAN) and to:

• train the GAN based on labeled image data, unlabeled image data, and generated image data generated by a generator of the GAN, wherein the GAN comprises a loss function that comprises error components for each of the labeled image data, unlabeled image data, and generated image data which is used to train the GAN;
• identify a new data source for which the trained GAN is to be adapted;
• adapt the trained GAN for the new data source;
• and
• classify image data in the new data source by applying the adapted GAN to the data in the new data source, wherein adapting the trained GAN comprises obtaining a minimized set of labeled images and utilizing the minimized set of images to perform the adapting of the trained GAN.

An apparatus comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement a generative adversarial network (GAN) and to:
• train the GAN based on labeled image data, unlabeled image data, and generated image data generated by a generator of the GAN, wherein the GAN comprises a loss function that comprises error components for each of the labeled image data, unlabeled image data, and generated image data which is used to train the GAN;
• identify a new data source for which the trained GAN is to be adapted;
• adapt the trained GAN for the new data source;
• and
• classify image data in the new data source by applying the adapted GAN to the data in the new data source, wherein adapting the trained GAN comprises obtaining a minimized set of labeled images and utilizing the minimized set of images to perform the adapting of the trained GAN.