SCIENTIA SINICA Informationis, Volume 48, Issue 7: 888-902(2018) https://doi.org/10.1360/N112017-00290

Double discriminator generative adversarial networks and their application in detecting nests built in catenary and semisupervized learning

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  • ReceivedMar 20, 2018
  • AcceptedMay 3, 2018
  • PublishedJul 20, 2018


In image-based detection of catenary anomalies, detection of bird's nest anomalies is a typical situation. However, the image data containing the nests is only a small portion of the total data, which makes nest detection a typical problem of imbalanced data classification. For using a machine learning algorithm to solve imbalanced data classification, the learning ability of data features is of much importance. Generative adversarial networks (GANs) can learn prosperous data features from unlabeled data, which has been widely confirmed and applied. Nonetheless, because of the limitation of GANs' structure and theory, it is not an ideal model for image classification. In this research, the GANs model is improved with respect to image classification tasks. The improved model is named "double discriminator generative adversarial networks" (DDGANs). With DDGANs, the classification results of nest detection are satisfactory, and it is also an effective semisupervized learning model. Experiments on the MNIST standard dataset show that the accuracy and convergence rate have been significantly improved compared with other models.

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