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      OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering

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          Abstract

          We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a generative adversarial network and a variational autoencoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once. The input to the training scheme is a varied collection of unlabeled images from the same domain, as well as a set of background images without a foreground object. In addition, the image generator can mix the background from one image, with a foreground that is conditioned either on that of a second image or on the index of a desired cluster. The method obtains state of the art results in comparison to the literature methods, when compared to the current state of the art in each of the tasks.

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          Author and article information

          Journal
          31 December 2019
          Article
          1912.13471
          9eef73e5-df02-4b3a-a19f-cf3bb62b3255

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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