Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine images of an object moving relative to a viewer of the object. The generator sub-network also is configured to generate one or more distribution-based images based on the images that were examined. The system also includes a discriminator sub-network configured to examine the one or more distribution-based images to determine whether the one or more distribution-based images accurately represent the object. A predicted optical flow of the object is represented by relative movement of the object as shown in the one or more distribution-based images.

Abstract: A generative adversarial network (GAN) system includes a generator neural sub-network configured to receive one or more images depicting one or more objects. The generator neural sub-network also is configured to generate a foreground image and a background image based on the one or more images that are received, the generator neural sub-network configured to combine the foreground image with the background image to form a consolidated image. The GAN system also includes a discriminator neural sub-network configured to examine the consolidated image and determine whether the consolidated image depicts at least one of the objects. The generator neural sub-network is configured to one or more of provide the consolidated image or generate an additional image as a training image used to train another neural network to automatically identify the one or more objects in one or more other images.

Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.

Abstract: A generative adversarial network (GAN) system includes a generator neural sub-network configured to receive one or more images depicting one or more objects. The generator neural sub-network also is configured to generate a foreground image and a background image based on the one or more images that are received, the generator neural sub-network configured to combine the foreground image with the background image to form a consolidated image. The GAN system also includes a discriminator neural sub-network configured to examine the consolidated image and determine whether the consolidated image depicts at least one of the objects. The generator neural sub-network is configured to one or more of provide the consolidated image or generate an additional image as a training image used to train another neural network to automatically identify the one or more objects in one or more other images.

Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.

Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine images of an object moving relative to a viewer of the object. The generator sub-network also is configured to generate one or more distribution-based images based on the images that were examined. The system also includes a discriminator sub-network configured to examine the one or more distribution-based images to determine whether the one or more distribution-based images accurately represent the object. A predicted optical flow of the object is represented by relative movement of the object as shown in the one or more distribution-based images.