Method and system for an adversarial training using meta-learned initialization

A system including a machine-learning network, comprising:

• an input interface configured to receive input data from a sensor, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor;
• a processor, in communication with the input interface, wherein the processor is programmed to:
• receive an input data from a sensor, wherein the input data includes an image;
• generate an adversarial version of the input data, utilizing a generator, in response to the input data;
• create a training data set utilizing the input data and the adversarial version of the input data;
• determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification;
• and
• update a classifier in response to the cross-entropy classification loss utilizing the training data set.

A computer-program product comprising a non-transitory computer readable medium storing instructions which, when executed by a computer, cause the computer to:

• receive an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor;
• generate an adversarial version of the input data, utilizing a generator, in response to the input data;
• create a training data set utilizing the input data and the adversarial version of the input data;
• determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification utilizing a classifier;
• and
• update the meta model and the classifier in response to the cross-entropy classification loss utilizing the training data set.