# Tag Info

## Hot answers tagged normalizing-flow

12 votes
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### Difference between invertible NN and flow-based NN

After some more reading I came to following conclusion: Invertible NN are just neural networks that represent bijective functions $f$. Normalizing flows are invertible NN $f$ that also have a ...
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7 votes
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• 208
3 votes
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### Planar Flow in Normalizing Flows

For every $z,$ notice that the displacement from $z$ to its destination $f(z),$ given by $f(z)-z,$ is a multiple of the fixed vector $u.$ Thus, if you were to diagram the effect of $f$ by drawing ...
• 316k
2 votes

### Planar Flow in Normalizing Flows

The equation $$\mathbf{w}^T\mathbf{z_1}+b=0$$ defines a (hyper)plane. The vector $\mathbf{w}$ is the normal vector. For a refresher on multivariable calculus, see here. So what happens if you have ...
• 20.2k
2 votes

### Normalizing Flows KL divergence equivalency

The answer to your first question follows from the fact that the Kullback-Leibler divergence is, under mild conditions, invariant under transformations. This is straightforward and is shown in the ...
• 10.5k
1 vote
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### Normalizing Flows Invertibility

I think it is important to recognize again that the so called coupling layers are splitted into two. One part directly passes to next layer without any modifications (i.e. $\pmb x_{1:d}$). That's why ...
• 36
1 vote

### Normalizing Flows KL divergence equivalency

In short The Kullback-Leibler divergence is the expectation value of the log-odds of two distributions $$D_{KL}(A || B) = \textbf{E}_A\left[\log \left(\frac{P_A(x)}{P_B(x)} \right) \right]$$ or for ...
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1 vote
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### Which parameters are updated in VAE with normalizing flow?

You optimize the loss with respect to $\theta$ and $\phi$—which includes the parameters of the decoder, the encoder, and the flow model. The source code in the blog post you've linked to answers the ...
• 8,437
1 vote
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### What is multi-scale architecture?

CNNs are typically constructed by stacking convolutional layers on top of each other, with each convolutional layer taking in the previous feature map and producing a successive feature map. The ...
• 25.6k
1 vote

### Inference in Normalizing Flow model: NICE(non linear independent components estimation)

Since $f$ is bijective, you could also implement its inverse $f^{-1}(h) = x$. For example, to invert the first layer of the network $h_{I_1}^1 = x_{I_1}, h_{I_2}^1 = x_{I_2} + M(x_{I_1})$, you can ...
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