Some cases I've seen lately seem to forgo this not out of ignorance but as a form of eletism/knowledge gate keeping.
It's a natural tendency for ingroups. Nearly any video game forum, or anything else that's full of hobbyists will ultimately contain posts that are absolutely full of acronyms. And they're impenetrable. Bear in mind, I'm not defending this behavior, and certainly not disagreeing with you.
And I'm not saying this is _my_ solution, this was literally taught to me in engineering first year.
If you're writing a paper, define every acronym the first time you use it.
If you're in a forum with a set of acronyms known to all, define them in a sticky or the forum readme.
For example: images generated by convolutional neural networks (CNN) are easy to identify.
May be this is specific to I.T or Computer Science? Where there are thousands of abbreviations and acronyms which itself is often the name people use. SQL, DRAM, CPU, HTTP, SRAM, FPGA, URL, TCP/IP, UDP, NAT, DHCP, GPL, etc.
I mean if you are discussing technicals of Neural network you expect your audience to at least know CPU, GPU, and FPGA. And if you are discussing software development I hope I dont have to spell out GPL.
So I dont think it is a form of eletism/knowledge gate keeping. In the age of internet you can search those "acronyms" meant without the full name, which isn't something could be easily done 15 to 20 years ago.
In other industry such as Mobile Wireless Networking, those acronyms are often clearly spell out because there are comparatively little of it. FDD, TDD, MIMO, NR or LTE are often spelt out in full when they first use.
It doesn't have to be a hard rule, but major topics of a subject should be spelled out, at least, then you're giving people something to work with in their web search.
I'm my university first-year CS class, a third of the students had never heard of GitHub. Now, that's easy to look up, and GPL seems to be a lucky acronym as well, but CNN certainly isn't. Expanding it the first time or adding a footnote costs you nothing, but people not right in your field or still learning tremendously. Someone who got their Master's in CS 10 years ago likely wouldn't have heard of CNNs at all, and neither would most new CS students.
A scientific publication in such a broad field with such a widely-applicable topic and one of the most clashing acronyms right in the title should most certainly at least expand their key terms.
UK reports rampant student marijuana use before class
That headline has quite a different meaning if “UK” is abbreviating “United Kingdom” versus “University of Kentucky”.
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!
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.
https://trends.google.com/trends/explore?geo=US&q=%2Fm%2F0x2...
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 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.
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.
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.
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.
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.
Did they really hoped for that their paper will remain in a specific group of experts? I seriously doubt so.
And the website isn't published in the CVPR, it's published on the internet.
Edit: Although, I see it does in first use in the introduction, so maybe that's just conforming to whoever's style guide.
"However, these methods represent only two instances of a broader set of techniques: image synthesis via convolutional neural networks (CNNs)."
Would this be receiving as much attention if they had used "Convolutional Neural Networks" instead of just CNN?
It's true that once you've trained your CNN you could make a non-convolutional NN that computes exactly the same things but less efficiently, but the point of an NN is not just what it can compute -- there are lots of systems that can, given enough parameters, approximate arbitrary functions well -- but how you train it.