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What a Deep Neural Network thinks about selfies
21–30 of 56 posts
Re: What a Deep Neural Network thinks about selfies
#22Earlier quoted context omitted.
Yeah, female users probably post more pictures and also probably have more friends.
This also would be controlled for by the tools the blog author used though—if a women has more friends, then they would also probably get more likes on all of the rest of their images. Not sure if posting more photos would drive the average up or down, but it would probably drive the "above the baseline" selfies in the same way.
More friends, but then the likes are not uniformly distributed with the increase of friends. Also more pictures means the "best picture" could be more of an outlier.
So best pictures might rise further above baseline for that person. That is, top picture gets 1000 likes, but most pictures get zero. Sort of like Zipfian distribution of words.
Anyway, these things are actually really hard to control for particularly because different types of friends/people have different effects on the likes. Now add to this cultural differences between countries/states/universities/rural-urban, etc.
I think the best method that is actually practical was the one okcupid did at some point with "my best face" where you rate a bunch of people's pictures and they rate yours. Then you figure out what pictures are good from the data.
If they kept the data for all these contests, it would be much easier to interpret in aggregate.
Re: What a Deep Neural Network thinks about selfies
#23I think it's less about the head getting chopped than about having "the head take up about 1/3 of the image," as Karpathy says. So what the net is learning is composition, or balance in an image, which is really cool. The rule of thirds is actually pretty well know to people in photography: https://en.wikipedia.org/wiki/Rule_of_thirds (Our deep-learning framework http://deeplearning4j.org missed his list, but it's go…
possibly, but none of the cropped examples have cropped chins. It's also well known in photography that you can cut off someone's forehead, but never their chin.
Re: What a Deep Neural Network thinks about selfies
#24A guide on how to take a good selfie that others will like: be female be blonde be attractive Incidentally, Christian Rudder did a really good "study" on the dating site pictures a few years ago: http://blog.okcupid.com/index.php/dont-be-ugly-by-accident/
A better guide on how to take a selfie: Don't take a selfie.
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Re: What a Deep Neural Network thinks about selfies
#25I would like to see a deep dream selfie ... Feed it an initial picture (noise, clouds, a selfie) and then backwards manipulate the input to maximize the assessed quality of the "selfie". I guess that would look pretty funny.
He did run something like that for cropping. He showed his favourite two "rude" ones at the bottom, where the 'Net cropped out the face of the person taking the selfie.
Re: What a Deep Neural Network thinks about selfies
#26[deleted]
Re: What a Deep Neural Network thinks about selfies
#27>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…
Well, one of the other factors is long hair and the tendency to oversaturate the face. Those factors don't seem independent to me, men are less likely to sport long hair and they're also less likely to oversature the face to measure up to some skin perfection standards (think of it as the photographic equivalent of makeup).
> but it can't be the real reason
Can't? Ontop of the above-listed aspects it is entirely possible that there is a bias that both sexes find female appearance somewhat more aesthetically pleasing.
Similar to how focus group testing for computer voices tends to result in female voices being chosen (at least that's what I often hear, couldn't find a solid source).
Even if the bias is small the correlated factors would amplify it when you're optimizing for a maximum, i.e. for the top selection.
Re: What a Deep Neural Network thinks about selfies
#28A guide on how to take a good selfie that others will like: be female be blonde be attractive Incidentally, Christian Rudder did a really good "study" on the dating site pictures a few years ago: http://blog.okcupid.com/index.php/dont-be-ugly-by-accident/
And, if you are female, chop off you forehead.
Re: What a Deep Neural Network thinks about selfies
#29One caveat with these machine inspired knowledge: they are prone to error, probably more than humans, at least for now. 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,…
What type of handcrafted optimizations are you talking about here?
The state of the art I've read about* (deep CNNs) in later years rely more on generalized tricks like augmenting the training data (artificially inflating the data set), pre-training and fine-tuning, ReLU, regularization methods like dropout, etc.
For anyone interested, here [1] are some benchmarks.
* Late night here, but often in the vein of this [0] work.
[0]: https://www.cs.toronto.edu/~ranzato/publications/taigman_cvp...
Re: What a Deep Neural Network thinks about selfies
#30A really good read. Good intro to ConvNets, a well designed and implemented test. Ad funny.