![]() ![]() As we condition both networks to the ground Generator based on these scores, the Generator improves its Image and each segmented image and sends its score feedback to the Images to make them resembling the ground truth a second network,Ĭalled Discriminator, measures the differences between the ground truth Players game: a first network, called Generator, learns to segment Networks (DCN) to automate the identification and mapping of fractureĪnd fault traces in optical images. We develop a novel method based on Deep Convolutional.Seminars GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULTĪND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE
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