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Automatic Labeling of Industrial Images by using Generative Adversarial Networks

This paper presents the combination of the adversarial nets framework with a kernel-based statistical dependency measurement for learning interpretable representations without any labeled data and with an autoencoder. Thus, the loss function consists of three terms and, based on the loss function, a new architecture emerges. Experiments show the functionality and potential of the architecture and the proposed kernel-based statistical dependency criterion quantitatively and qualitatively. Furthermore, the improvements are presented on a dataset consisting of industrial images.