Method and system for learning joint latent adversarial training

A system including a machine-learning network, comprising:

• an input interface configured to receive input data from a sensor, wherein the sensor includes a camera, a radar, a sonar, or a microphone;
• a processor, in communication with the input interface, wherein the processor is programmed to:
• receive the input data, wherein the input data includes paired cleaned-perturbed data, wherein the input data is indicative of image, radar, sonar, or sound information;
• generate a perturbed version of the input data, utilizing a generator and the input data;
• create a paired training data set utilizing data from the input data and a perturbed data utilizing the perturbed version of the input data;
• train the generator and a classifier jointly utilizing the paired training data set;
• and
• determine a latent vector utilized to generate one or more perturbations configured to maximize classification loss of a classifier;
• output a trained generator and a trained classifier upon convergence to a first convergence threshold.

A computer-program product storing instructions which, when executed by a computer, cause the computer to:

• receive an input data, wherein the input data includes a data set including paired clean data and perturbed data;
• generate a perturbed version of the input data utilizing a generator;
• create a training data set utilizing the input data and a perturbed image;
• train the generator and a classifier utilizing the training data and the perturbed version of the input data;
• and
• output a trained generator and a trained classifier upon convergence to a first threshold.