Entropy Minimization by Adverarial Learning

• A limitation of the Entropy loss is related to the absence of structural dependencies between local semantics.
• This is caused by the aggregation of the pixel-wise prediction entropies by summation.
• unified adversarial training framework which minimizes indirectly the Entropy of target data, by encouraging it to become similar to the source one.
• minimizing distribution distance between source and target on the weighted self-information space
• We perform the adversarial adaptation on weighted self-information maps using a fully-convolutional discriminator network
• the discriminator produces domain classification outputs, i.e., class label for the source (resp. target) domain.
• discriminate outputs coming from source and target images, and at the same time, train the segmentation network to fool the discriminator.