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What is being optimized with WGAN loss? Is the generator maximizing or minimizing the critic value?

I think I understand what's happening with the loss functions now. Notation: D = discriminator/critic G = generator D(x) - Critic score on real data D(G(z)) - Critic score on fake data ∇_D - Critic ...
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What is the meaning of $p_{\text {data }}(y)$ in the CycleGAN?

I interpret $p_{data}(y)$ as the empirical probability of seeing an image $y$ in the training data. For example, in a typical training run, each training image is shown to the network the same number ...
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What is the domain of the discriminator of a GAN?

Let me try to explain this way, comment if you think it's incorrect. Assume a simple linear function, $y=f(x)=ax+b$ where $a \in \mathbb{R}^*$ and $b\in \mathbb{R}$, each value of $y$ is unique, which ...
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What is the domain of the discriminator of a GAN?

Formally, for an input $x$, $D(x)$ gives you the probability of $x$ being real. In this sense $D:\mathcal{X}\rightarrow [0,1]$, where $\mathcal{X}$ is the input space. That said, the output of the ...
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Why do we use the same parameters for the joint, marginal and conditional distributions in VAEs?

I think this is very confusing to many people. I had to deal with VAEs (and Bayesian neural networks) multiple times in the past, and I've seen so many inconsistent notations and unclear explanations. ...
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Why do we train the discriminators k times but train the generator only 1 time in a iteration in GAN?

The answer to your question can be found in [1, sec. 4.4]. Briefly, the GAN optimization problem is a mini-max game, and early on the proposition of GANs, the authors had the idea that one should ...
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