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Comment by usmannk | Hacker News Reader

This is a paper that was published in CVPR (Conference on Computer Vision and Pattern Recognition). In that context it is unambiguous that CNN means Convolutional Neural Networks.

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In that context, yes. But in the context of (eg) a person that just happened to drop here on HN for a quick read, it makes no sense in the headline.

Welcome to hacker news.

Someone who scanned the front page and didn’t delve into these comments might infere that the American news network CNN artificially generate images for their news stories. That’s how I picked this up.

Where artificially generated images are improbable. Oh wait.

Also, let me quote the linked page:

> malicious use of fake imagery is likely be deployed on a social media platform

Nope, that couldn't possibly relate to any news network. Never never never gonna admit that!

Where would you draw the line though? Would you want the same for e.g. HTML? HTTP? HN? YC?

I mean personally I'm all in favor of more usage - or even automatic insertion - of the `<abbr>` tag. Can probably be done with a browser addon as well.

One relatively easy to draw line is whether the abbreviation has other commonly used meanings. CNN has, while none of HTML, HTTP, HN or YC do.

That really depends on the reader...

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"CNN generated Image" sounds like "Images generated by the Cable News Network, CNN" as if the corporation has some software/policy for editing and prepping images in a way they can be detected. It's not so absurd, photojournalism can be quite specific in rules.

The ability to classify photos by news outlet based on identifying their photojournalism rules through computer algorithms sounds like a remarkably clever idea.

HTML doesn't have that ambiguity.

I wonder if part of an issue is the generation gap? For older readers, I imagine that they're much more familiar with CNN referring to the Cable News Network. Whereas for younger readers heavily involved in tech, me included, we aren't as heavily tied to the former abbreviation, so CNN referring to neural networks comes more readily to mind.

Well, I'm not "old" (which ever the definition is) yet. I'm just on the Front-End side of the dev world. Never fiddled with AI/Neural Networks.

It’s all about audience and confusion. My feel of the HN readership is that it’s a vey broad base of mostly technical backgrounds; international but US heavy. That puts the ones you list as perfectly reasonable, and CNN referring to the neural networks is usually ok.

I this case, however, there’s a conflict with the news network which could also plausibly be the subject of the headline. They have interenational recognizability, and have been using the acronym almost exclusively for years; it is effectively their name.

Maybe when it is a widely known abbreviation for something else?

We are not computer algorithms here. A human being can decide "yeah this sounds like cable news network" and use the long form of this CNN.

Somewhere between HTML and CNN. The crowd here is familiar enough with HTML but CNN is not only a smaller abbreviation but also an ambiguous one here.

> Where would you draw the line though? Would you want the same for e.g. HTML?

The day someone uses “HTML” to mean “hyper-threaded machine learning” or whatever, yes definitely.

CNN was unambiguously used for the TV channel for decades now, of course some people are confused when one uses it to mean something else without warning.

HTML has no alternative interpretation in widespread usage so it is appropriate for use in any context where average general knowledge can be assumed.

HN is suitable here because it can be assumed that Hacker News denizens are acquainted with a rather obvious shorthand for their own community.

YC... likely as above but might be safer explicated and explained.

Not really, I just thought that CNN (the news network) uses generated images in their articles. The topic about recognizing them, or even generating them, would make sense in CVPR.

If that were their justification, they wouldn't need to define the acronym in the paper. However, in the introduction section of the actual paper they do define CNN. But they use the acronym 9 times before defining it, which is what's kinda weird.

And the website isn't published in the CVPR, it's published on the internet.

I found CNN a bit confusing, even though I did guess it meant Convolutional Neural Net.

Perhaps my pre-caffeine morning brain is overly pedantic but Generative Nets use deconvolutions to generate images from latent codes, so using CNN rather than GAN (Generative Adversarial Network) is a bit confusing in this context.

CNNs are used by VAEs (Variational AutoEncoders, also generative) use convolutions to produce the latent codes and the discriminator (adversarial) part of GAN training uses convolutions.

I think Generative Networks ( or GNNs ;-) ) would perhaps have been clearer.

GANs use transposed convolutions to generate images. I haven’t seen anyone use a true deconvolution operation in deep learning. Not even sure it’s possible to invert the result of a convolutional layer, because it’s lossy.

"Transposed convolution" was called "deconvolution" for a short period of time. The authors apparently weren't aware that "deconvolution" has meant "the inverse problem of recovering a convolved signal" since at least the 70s.

Either the paper should not be presented elsewhere or should speak out the meaning!

Did they really hoped for that their paper will remain in a specific group of experts? I seriously doubt so.

Yeah, we are not in that context, aren't we?

Also, the original authors are not necessarily the ones that posted it in here.

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