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While most existing discriminators are trained to classify\ninput images as real or fake on the image level, we design a discriminator in a\nfully convolutional manner to differentiate the predicted probability maps from\nthe ground truth segmentation distribution with the consideration of the\nspatial resolution. We show that the proposed discriminator can be used to\nimprove semantic segmentation accuracy by coupling the adversarial loss with\nthe standard cross entropy loss of the proposed model. In addition, the fully\nconvolutional discriminator enables semi-supervised learning through\ndiscovering the trustworthy regions in predicted results of unlabeled images,\nthereby providing additional supervisory signals. In contrast to existing\nmethods that utilize weakly-labeled images, our method leverages unlabeled\nimages to enhance the segmentation model. Experimental results on the PASCAL\nVOC 2012 and Cityscapes datasets demonstrate the effectiveness of the proposed\nalgorithm.","url_abs":"http://arxiv.org/abs/1802.07934v2","url_pdf":"http://arxiv.org/pdf/1802.07934v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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