# Questions tagged [generative-models]

A probabilistic or statistical model thought about as describing how the values in a sample is actually generated, and not only as a description or approximation.

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### How does the reparameterization trick for VAEs work and why is it important?

How does the reparameterization trick for variational autoencoders (VAE) work? Is there an intuitive and easy explanation without simplifying the underlying math? And why do we need the 'trick'?
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### Generative vs. discriminative

I know that generative means "based on $P(x,y)$" and discriminative means "based on $P(y|x)$," but I'm confused on several points: Wikipedia (+ many other hits on the web) classify things like SVMs ...
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### The connection between Bayesian statistics and generative modeling

Can someone refer me to a good reference that explains the connection between Bayesian statistics and generative modeling techniques? Why do we usually use generative models with Bayesian techniques? ...
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### Likelihood-free inference - what does it mean?

Recently I have become aware of 'likelihood-free' methods being bandied about in literature. However I am not clear on what it means for an inference or optimization method to be likelihood-free. In ...
• 947
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### Generating distributions with with given variance, skewness, and kurtosis

I would like to generate distributions that are as close to normal as possible, except for the deviations shows below. The options I've located have properties that are related to the families of ...
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### Variational Autoencoder − Dimension of the latent space

I've done some experiments to understand the influence of the dimension of the latent space in a VAE, and it seems that the higher the space, the harder it is to generate realistic images. I might ...
• 263
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### Why use Binary Cross Entropy for Generator in Adversarial Networks

I'm trying to work with General Adversarial Networks and there's something I'm seeing everywhere but can't explain why... the GANs are usually constructed from a Generator (which usually generates an ...
• 515
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### Is the only difference between conditional generative models and discriminative models the complexity of the modeled distribution?

One common way to define discriminative models is that they model $P(Y|X)$, where $Y$ is the label, and $X$ is the observed variables. Conditional generative models do something quite similar, but the ...
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### When generating samples using variational autoencoder, we decode samples from $N(0,1)$ instead of $\mu + \sigma N(0,1)$
Context: I'm trying to understand the use of variational autoencoders as generators. My understanding: During training, for an input point $x_i$ we want to learn latent $\mu_i$ and $\sigma_i$ and ...