SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS

Abstract

The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representations of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications to train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications. Figure 1 is the representative figure.

Specification

WHAT IS CLAIMED IS:
1. A system for training a generative adversarial network using images including representations of a feature-of-interest, comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to perform operations comprising: provide a first plurality of images that include representations of the feature-of-interest and indicators of the locations of the feature-of-interest in images of the first plurality of images; using the first plurality of images and indicators of the feature-of- interest, train an object detection network to detect the feature-of-interest, optionally where the object detection network is a convolutional neural network; provide a second plurality of images that include representations of the feature-of-interest, the second plurality of images comprising a larger number of images than that included in the first plurality of images; apply the trained object detection network to the second plurality of images to produce a first plurality of detections of the feature-of-interest; provide manually set verifications of true positives and false positives with respect to the first plurality of detections; using the verifications of the true positives and false positives with respect to the first plurality of detections, train a generative adversarial network; and retrain the generative adversarial network using at least one further set of images and detections of the feature-of-interest, together with further manually set verifications of true positives and false positives with respect to the further detections of the feature-of-interest.
2. The system of claim 1 , wherein the at least one processor is further configured to retrain the generative adversarial network by providing verifications of false negatives for missed detections of the feature-of-interest in two or mor images.
3. The system of any preceding claim, wherein the number of images in the second plural

Documents