11–20 of 56 posts
https://en.wikipedia.org/wiki/Rule_of_thirds
(Our deep-learning framework http://deeplearning4j.org missed his list, but it's got working convnets, too.)
One thing I always found interesting is Lecun is credited with developing covnets, but Hinton is apparently credited with scaling them and showing the world how great they are in the paper from 2012 - why was Hinton's group (Toronto) able to publish these ground breaking results before Lecun's group (NYU)
Geoff Hinton had grad students who wanted to work on the problem, but Yann LeCun didn't.
"In about 2012, it should have been Yann's group, but Yann was unlucky, he didn't have a student who really wanted to do it. But we had a couple of students who wanted to do it and we took all of Yann's techniques and added some of our own."
One thing I always found interesting is Lecun is credited with developing covnets, but Hinton is apparently credited with scaling them and showing the world how great they are in the paper from 2012 - why was Hinton's group (Toronto) able to publish these ground breaking results before Lecun's group (NYU)
For example, if you train a CNN directly with human faces, its recognition rate comes way below what a human is capable of. Only after you apply tons of handcrafted optimizations, which are mostly black art, will you get close to or surpass a human's capability. Without much domain specific tuning, an AI's insight is far from reliable.
Earlier quoted context omitted.
For me, this thing about having the top of your head cut from the picture is new. Who would have thought..
Makes a bit of sense, in combination with the "be female" advice, cutting off the forehead puts the center of the photograph closer to her cleavage, and typically shows off her entire chest.
This sounds true, but it can't be the real reason—selfies are ranked relative to the other images by the same user. So unless users are taking a lot of #selfies of people of different genders, we can assume the dataset is already controlled for the gender of the person in the image, no? Unless there's some confounding factor at play, such as some demographic segment being more likely to optimize for good selfies occasionally but have boring feeds the rest of the time.
would be super interesting, if the data is available, to normalize this by exposure. Of the people that saw an image, how many clicked "like"?
>Be female. Women are consistently ranked higher than men. In particular, notice that there is not a single guy in the top 100. This sounds true, but it can't be the real reason—selfies are ranked relative to the other images by the same user . So unless users are taking a lot of #selfies of people of different genders, we can assume the dataset is already controlled for the gender of the person in the image, no? Unl…
>Be female. Women are consistently ranked higher than men. In particular, notice that there is not a single guy in the top 100. This sounds true, but it can't be the real reason—selfies are ranked relative to the other images by the same user . So unless users are taking a lot of #selfies of people of different genders, we can assume the dataset is already controlled for the gender of the person in the image, no? Unl…
Yeah, female users probably post more pictures and also probably have more friends.
Earlier quoted context omitted.
For me, this thing about having the top of your head cut from the picture is new. Who would have thought..
Makes a bit of sense, in combination with the "be female" advice, cutting off the forehead puts the center of the photograph closer to her cleavage, and typically shows off her entire chest.