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Aug 20, 2017 · The loss function of the vanilla GAN measures the JS divergence between the distributions of pr and pg. This metric fails to provide a meaningful value when two distributions are disjoint. Wasserstein metric is proposed to replace JS divergence because it has a much smoother value space. See more in the next section.

 
The Wasserstein distance serves as a loss function for unsupervised learning which depends on the choice of a ground metric on sample space. We propose to use the Wasserstein distance itself as the ground metric on the sample space of images.
Sep 01, 2019 · The choice of loss function is a hot research topic and many alternate loss functions have been proposed and evaluated. Two popular alternate loss functions used in many GAN implementations are the least squares loss and the Wasserstein loss. Least Squares GAN Loss
The WGAN (Wasserstein GAN) The Wasserstein GAN is considered to be an extension of the Generative Adversarial network introduced by Ian Goodfellow.WGAN was introduced by Martin Arjovsky in 2017 and promises to improve both the stability when training the model as well as introduces a loss function that is able to correlate with the quality of the generated events.
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissimilarity for probability ...
Feb 10, 2020 · Wasserstein Loss. By default, TF-GAN uses Wasserstein loss. This loss function depends on a modification of the GAN scheme (called "Wasserstein GAN" or "WGAN") in which the discriminator does not...

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Wasserstein loss function

W e evaluate our HCNN model and Wasserstein Dice loss functions on the task of brain tumour segmentation using BraTS’15 training set that provides m ul- timodal images (T1, T1c, T2 and Flair ... Buy thoughtful Smart Home products at incredible prices with free shipping and awesome customer service. Get your home into the new era with Wasserstein.

Aug 01, 2020 · Because the texture detail information can be represented by local binary pattern , we define a LBP loss function for the generator. Overall, the dual discriminators Wasserstein generative adversarial network and LBP loss function can encourage the fused image to keep rich texture information. with a loss function ‘(;) acting as a surrogate of E(;). 3.2 Optimal transport and the exact Wasserstein loss Information divergence-based loss functions are widely used in learning with probability-valued out-puts. Along with other popular measures like Hellinger distance and ˜2 distance, these divergences

Mar 17, 2019 · The idea of WGAN is to replace the loss function such that it is ensured that there always exists a non-zero gradient. It turns out that this can be done with the Wasserstein distance between the generator distribution and the data distribution. This is the WGAN discriminator’s loss function: Aug 20, 2017 · The loss function of the vanilla GAN measures the JS divergence between the distributions of pr and pg. This metric fails to provide a meaningful value when two distributions are disjoint. Wasserstein metric is proposed to replace JS divergence because it has a much smoother value space. See more in the next section. To tackle this issue, in this paper we propose a novel robust matrix regression model with imposing Wasserstein distances on both loss function and regularization. It successfully integrate Wasserstein distance into the regression model, which can excavate the latent geometry of cognitive data.

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