Apparatus and method for generating training data for a machine learning system

Abstract

The present disclosure provides an apparatus and method for training a machine learning engine configured to determine whether an object in a two dimensional (2D) image is in-scope or out-of-scope relative to the one or more 3D objects that includes receiving a 3D model of each of the one or more 3D objects, for each 3D model receiving a set of specifications and thresholds for the 3D model, augmenting the specifications of the 3D model to generate a plurality of augmented 3D models, and generating auxiliary training data based on the plurality of augmented 3D models, and utilizing the auxiliary training data to train the machine learning engine. The present disclosure also provides an apparatus and method for training a machine learning engine that includes receiving sampling parameters related to a system of interest, sampling a generative model of a generative adversarial network (GAN) based on the sampling parameters to generate auxiliary input data, inputting the auxiliary input data into a discriminator model of the GAN to generate an auxiliary label associated with each of the auxiliary input data element, and utilizing the auxiliary input data and auxiliary labels as auxiliary training data to train the machine learning engine.