<p>It's easier to understand in the context of the lecture. I'm assuming you're referencing this slide in <a href="http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf" rel="nofollow noreferrer">Lecture 12</a>:
<a href="https://i.sstatic.net/PJiWN.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/PJiWN.png" alt="CS231n Lec 12, slide 16: Taxonomy of Generative Modles: http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf" /></a></p>
<p>What distinguishes the FVBNs from the other explicit density models are that they have a <em>tractable density</em>: that is, that you can exactly calculate the probability of samples of your dataset (or at least, that's the assumption made by the model). <strong>This is what's meant by &quot;fully visible&quot;</strong>.</p>
<p>Take PixelRNN/CNN, for example. Here, you model images autoregressively, where a given pixel only depends on previous pixels. This allows you to decompose the distribution over many pixels using the chain rule. <a href="http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf" rel="nofollow noreferrer">Look at slide 23 in the lecture for more info.</a> <strong>The main idea is that this decomposition allows you to calculate the probability of a sample exactly, making it &quot;fully visible&quot;.</strong></p>
<p>The connection to deep belief networks is a bit more iffy in my opinion. The <a href="https://www.jmlr.org/papers/volume17/16-272/16-272.pdf" rel="nofollow noreferrer">NADE paper</a> explains how the NADE forward pass is similar to an approximation of a RBM. I'm unsure if this extends to other models, however.</p>

CC BY-SA 4.0
2023-09-04T00:19:32.647Z
2023-09-09T05:40:49.047Z
2023-09-09T05:40:49.047Z
Alexander Wan
<p>In CS231n, I saw the terminology &quot;fully visible belief network&quot;, one category of explicit density generative model such as PixelCNN and NADE.</p>
<p>Although I can understand what this terminology wants to say, but I need more explanations in detail:</p>
<ol>
<li>What exactly does &quot;fully visible&quot; (or &quot;visible&quot;) mean?</li>
<li>I have searched about deep belief net, and I believe that it is just a model in category of deep neural network but trained in unsupervised way with energy function. Am I right?</li>
</ol>

CC BY-SA 4.0
2023-09-01T23:19:29.107Z
deep belief net has little to do with neural network, if not sharing the concept of "neurons" but they are defined in 2 very different ways, though, i have no idea what they mean with _fully visible belief network_... maybe having no "hidden/latent variables", but it's a pretty loose definition
Alberto
<generative-model>
What does "fully visible belief network" stand for?
Chris
# What does "fully visible belief network" stand for?

(Asked by: username_0 on 2023-08-30)

In CS231n, I saw the terminology "fully visible belief network", one category of explicit density generative model such as PixelCNN and NADE.

Although I can understand what this terminology wants to say, but I need more explanations in detail:

1. What exactly does "fully visible" (or "visible") mean?
2. I have searched about deep belief net, and I believe that it is just a model in category of deep neural network but trained in unsupervised way with energy function. Am I right?

**Tags:** generative-model

> **Comments:**
> **username_1**: deep belief net has little to do with neural network, if not sharing the concept of "neurons" but they are defined in 2 very different ways, though, i have no idea what they mean with _fully visible belief network_... maybe having no "hidden/latent variables", but it's a pretty loose definition  

---

## 1 answers

### Answer by username_2 on 2023-09-04. Score: 1

It's easier to understand in the context of the lecture. I'm assuming you're referencing this slide in [Lecture 12](http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf):
[![CS231n Lec 12, slide 16: Taxonomy of Generative Modles: http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf](https://i.sstatic.net/PJiWN.png)](https://i.sstatic.net/PJiWN.png)

What distinguishes the FVBNs from the other explicit density models are that they have a *tractable density*: that is, that you can exactly calculate the probability of samples of your dataset (or at least, that's the assumption made by the model). **This is what's meant by "fully visible"**.

Take PixelRNN/CNN, for example. Here, you model images autoregressively, where a given pixel only depends on previous pixels. This allows you to decompose the distribution over many pixels using the chain rule. [Look at slide 23 in the lecture for more info.](http://cs231n.stanford.edu/2021/slides/2021/lecture_12.pdf) **The main idea is that this decomposition allows you to calculate the probability of a sample exactly, making it "fully visible".**

The connection to deep belief networks is a bit more iffy in my opinion. The [NADE paper](https://www.jmlr.org/papers/volume17/16-272/16-272.pdf) explains how the NADE forward pass is similar to an approximation of a RBM. I'm unsure if this extends to other models, however.


---

