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Interesting topic, tedious article. Paraphrasing: Q: What's a generative model? A: Well, we have these neural nets and... Ugh. I understand the excitement fo

Interesting topic, tedious article. Paraphrasing: Q: What's a generative model? A: Well, we have these neural nets and... Ugh. I understand the excitement for one's own research but if the point is to make these results accessible to a wider audience then it's important not to get lost in the details, at least not right away. IMO, there's very little here in the way of high-level intuition. If I did not already have a PhD, and some exposure to ML (not my area), I would probably find this article entirely indecipherable. Again, paraphrasing: Q: OK, so I understand you want to create pictures that resemble real photos. And you really like this DCGAN method, right? A: Yes! See, it takes 100 random numbers and... Come on guys. You can do better.

resu_nimda 10y ago
FWIW, I found this comment pretty indecipherable. I have no idea how your examples illustrate your point. Maybe you can do better as well? Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive.
- gradstudent 10y ago
  >Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive. There's nothing especially derisive in my assessment. I don't think the content is bad, just boring. I also think it's too technical for a non-specialist audience. > Maybe you can do better as well? My first criticism is that generative models are not something specific to neural nets but that's not obvious from the article. My second criticism is that their explanations are overly mechanical. In the case of DCGAN the article begins by talking about parameters and magic numbers; i.e. they explain how the thing works rather than what it does, at an intuitive level. Clear enough?
  - platz 10y ago
    Listen to gradstudent, he knows what he's talking about.. this article presents generative models as a grab bag of NN algorithms that came out this quarter.
    - visarga 10y ago
      Most papers are migrating from plain classification to variational methods. This is the new trend. That means, instead of just predicting labels, they know also predict a probability for each label, a degree of confidence. And they work both ways, they can generate, as well as classify. And they are unsupervised, which means they can benefit from tons of data laying around.
      - platz 10y ago
        Your definition of "variational methods" is actually a definition of generative models. https://en.wikipedia.org/wiki/Generative_model https://en.wikipedia.org/wiki/Generative_model. variational methods is a much more specific concept, and your answer speaks more to the general and high-level concept of generative models. Notice on the wikipedia page for generative models, there is a lot more than variational methods.
  - visarga 10y ago
    > I don't think the content is bad, just boring It's talking about machines gaining the power of imagination. How is that boring?
    - gradstudent 10y ago
      > It's talking about machines gaining the power of imagination. How is that boring? It does nothing of the sort! If anyone comes away with this conclusion I would say the article has failed entirely. Which, btw, is my whole point: there's no over-arching intuitive explanation of what generative models are, why they're interesting or even what concrete problem they're solving.
  - choosername 10y ago
    >they explain how the thing works rather than what it does In some kinds of logic this is impossible, but isn't that how reports are usually written? The what is the conclusion of the how. Many papers omit the mechanics completely and get critized for it, too.
visarga 10y ago
I have been reading up on ML papers for months, and found the article pretty basic. It just gave a nice overview of the state of the art. If anything, it didn't go deep enough to get to the real interesting bits. From their perspective, it's hard to put such information in an accessible format. Try explaining redux for example, to a person who has no idea what functional programming is. How would you do it?
choosername 10y ago
>if the point is to make these results accessible to a wider audience It is not. While it's a big, growing field, it's really a narrow audience that can be expected to understand this, far from everyone in the field. How intuitive the writing appears is subjective. I'm sure I don't understand a word of it, not just for lack of intuition.

resu_nimda 10y ago

FWIW, I found this comment pretty indecipherable. I have no idea how your examples illustrate your point. Maybe you can do better as well? Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive.

gradstudent 10y ago
>Which is to say, effectively communicating something technical to a diverse audience is difficult, let's not be unnecessarily derisive. There's nothing especially derisive in my assessment. I don't think the content is bad, just boring. I also think it's too technical for a non-specialist audience. > Maybe you can do better as well? My first criticism is that generative models are not something specific to neural nets but that's not obvious from the article. My second criticism is that their explanations are overly mechanical. In the case of DCGAN the article begins by talking about parameters and magic numbers; i.e. they explain how the thing works rather than what it does, at an intuitive level. Clear enough?
- platz 10y ago
  Listen to gradstudent, he knows what he's talking about.. this article presents generative models as a grab bag of NN algorithms that came out this quarter.
  - visarga 10y ago
    Most papers are migrating from plain classification to variational methods. This is the new trend. That means, instead of just predicting labels, they know also predict a probability for each label, a degree of confidence. And they work both ways, they can generate, as well as classify. And they are unsupervised, which means they can benefit from tons of data laying around.
    - platz 10y ago
      Your definition of "variational methods" is actually a definition of generative models. https://en.wikipedia.org/wiki/Generative_model https://en.wikipedia.org/wiki/Generative_model. variational methods is a much more specific concept, and your answer speaks more to the general and high-level concept of generative models. Notice on the wikipedia page for generative models, there is a lot more than variational methods.
- visarga 10y ago
  > I don't think the content is bad, just boring It's talking about machines gaining the power of imagination. How is that boring?
  - gradstudent 10y ago
    > It's talking about machines gaining the power of imagination. How is that boring? It does nothing of the sort! If anyone comes away with this conclusion I would say the article has failed entirely. Which, btw, is my whole point: there's no over-arching intuitive explanation of what generative models are, why they're interesting or even what concrete problem they're solving.
- choosername 10y ago
  >they explain how the thing works rather than what it does In some kinds of logic this is impossible, but isn't that how reports are usually written? The what is the conclusion of the how. Many papers omit the mechanics completely and get critized for it, too.

visarga 10y ago

I have been reading up on ML papers for months, and found the article pretty basic. It just gave a nice overview of the state of the art. If anything, it didn't go deep enough to get to the real interesting bits. From their perspective, it's hard to put such information in an accessible format. Try explaining redux for example, to a person who has no idea what functional programming is. How would you do it?

choosername 10y ago

>if the point is to make these results accessible to a wider audience It is not. While it's a big, growing field, it's really a narrow audience that can be expected to understand this, far from everyone in the field. How intuitive the writing appears is subjective. I'm sure I don't understand a word of it, not just for lack of intuition.