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.
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.
"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.
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.
> 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.
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.
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.
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.