Abstract: Described herein are systems and methods for generating multi-class machine learning classifiers using a plurality of hyper-trained binary classifiers to perform both characteristic detection and object type classification. A range associated with a characteristic of the data sought to be classified may be divided into a plurality of sub-ranges that can be ordered. A plurality of binary machine learning classifiers can be generated, with each binary classifier associated with one of the sub-ranges. Each binary classifier of the plurality of classifiers may be generated by training the classifier to determine if a given input data set contains a characteristic within or outside of the sub-range associated with the binary classifier. Once the binary classifiers have been trained, they can be used to determine what value within the range of the characteristic an input data sample has, and may be used to determine other properties about the input data.

Abstract: Presented herein are systems and methods for generating synthetic training data for machine learning models. Images of a particular object (such as an aircraft) can be received and processed to cutout the object (i.e., separate the object from the background) from the received image. The systems and methods described herein can detect areas in the background images to place an object. Once a suitable area has been detected, the cutout object image can be superimposed on the background image at the location determined to be suitable for placing the object. Superimposing the object onto the background image can include blending the two images using a plurality of blending techniques to reduce artifacts that may bias a supervised training process.