# Questions tagged [wasserstein]

The Wasserstein metric or Earth Movers Distance is a distance function between probability distributions.

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### Can we train normalizing flows with Wasserstein distance?

To train flow based models, you usually either use forward or reverse kl as your loss function. My question is, can you use wasserstein distance directly as your loss function to replace kl? I have ...
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### Problem for training Wasserstein GAN

I'm trying to train a Wasserstein GAN to guess sparse one-hot encoded matrices (0/1), in particular I've reimplemented the same architecture proposed in this paper. The problem, as you can see, is ...
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in the paper, its mentioned that the authors used L2 regularization during training as shown below. −1/4ε(r(y) − r(x) − d(x, y))2 but it is not clear to me how to implement it, any hints?.
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### Getting rid of the additive degree of freedom for discriminators of WGAN-GP's

Setting: Discriminators in WGAN-GP's are trained to minimise the following loss functional over functions D: Here I have been playing around with training a critic (simple convolutional network ...
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### Why is "weight clipping" needed for Wasserstein GANs?

I am reading the original paper on the Wasserstein GAN: https://arxiv.org/pdf/1701.07875.pdf and I came across this paragraph: I don't understand the statement: "$\mathcal{W}$ is compact implies ...
1 vote
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### Statistical estimators distance for close empirical distributions

Is it valid to argue that two empirical distributions $p_1, p_2$ having small Wasserstein distance $W_r(\cdot)$ for an order $r$ will yield close MLE estimators for a statistical model ...
1 vote
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### In a WGAN, when is the generator's loss function ever used? [closed]

I've been building a Wasserstein GAN in Keras recently following the original Arjovsky implementation in PyTorch and ran across an issue I've yet to understand. To my knowledge, the critic network is ...
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### Distance measure between discrete distributions (that contains 0) and uniform

I'm trying to choose a district metric that falls between 0 to 1 and lets me compare the distance between a uniform probability distribution and any given probability distribution (could be random, ...