Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation

4 May 2016Mehran JavanmardiMehdi SajjadiTing LiuTolga Tasdizen

We introduce a novel unsupervised loss function for learning semantic segmentation with deep convolutional neural nets (ConvNet) when densely labeled training images are not available. More specifically, the proposed loss function penalizes the L1-norm of the gradient of the label probability vector image , i.e. total variation, produced by the ConvNet... (read more)

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