I should update my sexy map finder: <a href="http://exclav.es/2016/05/20/sexy-maps/" rel="nofollow">http://exclav.es/2016/05/20/sexy-maps/</a>
Forgive my ignorance of ML but the last bit: "you'll need your own porn to train on" confused me. Does this mean that they're just exposing the rough topology of their neutral net (eg depth) and not the actual weights between nodes? I'm curious to learn from an ML expert how much this actually offers.
Direct link to Github: <a href="https://github.com/yahoo/open_nsfw" rel="nofollow">https://github.com/yahoo/open_nsfw</a>
Has anyone tried taking the features that are learned at the various layers of a neural net and feeding them into something like this: <a href="https://news.ycombinator.com/item?id=12612246" rel="nofollow">https://news.ycombinator.com/item?id=12612246</a>?<p>I imagine we would get some really interesting images back...
We are not releasing the training images or other details due to the nature of the data, but instead we open source the output model which can be used for classification by a developer.<p>I'm guessing the one who had to input the data/images had a fun time at work :p
They acknowledge that NSFW (or pornographic) is hard to define, a la 'I recognize it if I see it'.<p>But looking at the meager 3 sample images I'm confused about the scoring already. Why is the one in the middle scoring the highest?<p>The question is an honest one. The two rightmost images seem to be interchangeable to me and are ~boring~: People at the beach. Is this network therefor already trained to include the biases of the creators?
My first thought was from years ago, when I was pitching open source forensic services to London police (did not get far, bad Salesman that I am)<p>Cataloging, categorising pornography seized is a nasty job and one that cops across the planet might do better with good common OSS tools.<p>Hopefully this will help
My first thought: would probably be very useful for sites to crack down on inappropriate content.<p>My second thought: I could probably use this to find porn in unexpected places via a webscraping Python program.
Good to see they've automated this (beyond the initial classification of training data). In the early days of the web, such filters were typically based on manually maintained lists of sites. I actually met someone at a party once whose full-time job was to surf for porn, to maintain the filter for a provider of IT services to schools (he worked for a company now called RM Education). He said it was his ideal job for the first few days, but soon grew tiresome (note that back in those days there wasn't really any extremely objectionable material on the web).
I'm not a deep learning person whatsoever, but I do have an interesting use case that I won't disclose publicly: Is there a way to build this, and output detections based on the, ugh, object it has detected?<p>e.g.<p>penis 0.94<p>vagina 0.01
Aren't there more important problems to work on than worrying about someone looking at naked people? This is just what we need: more effort spent on censoring and controlling people.
Reminds me of this post from hackerfactor where he describes his own porn filter based on pHash.<p><a href="http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html" rel="nofollow">http://www.hackerfactor.com/blog/index.php?/archives/529-Kin...</a><p>It'd be interesting to see a direct comparison of the two. Off the cuff, I'd expect the deep neural network to be more accurate and better at generalizing, but much more expensive to train.
another work in this field: "Adult video content detection using Machine Learning Techniques" PDF: <a href="http://colorlab.no/content/download/37238/470343/file/VictorTorres_MasterThesis.pdf" rel="nofollow">http://colorlab.no/content/download/37238/470343/file/Victor...</a>
Awesome!<p>I have been using nude.js to do this ( <a href="http://s.codepen.io/icodeforlove/debug/gMrEKV" rel="nofollow">http://s.codepen.io/icodeforlove/debug/gMrEKV</a> ), which is hit or miss.
To be precise they are only releasing the already trained model. The associated dataset is not being made public.<p>Thus, it is meant to be for off the shelf use rather than being able to tinker with the network to produce nuanced results.
I wonder what would happen if we stopped firing people for watching NSFW images. I mean bosses look at NSFW images all the time and it sounds like a shallow reason to fire someone.
I would suggest, that the link should go to Yahoo's blog post<p><a href="https://yahooeng.tumblr.com/post/151148689421/open-sourcing-a-deep-learning-solution-for" rel="nofollow">https://yahooeng.tumblr.com/post/151148689421/open-sourcing-...</a><p>which contains some technical details. (And furthermore, I guess the HN crowd has enough Internet experience to come up with stupid jokes of their own design.)
The Yahoo blog[1] post is far more interesting than this techcrunch "article". Suggest changing URL to the Yahoo Blog please.<p>[1] <a href="https://yahooeng.tumblr.com/post/151148689421/open-sourcing-a-deep-learning-solution-for" rel="nofollow">https://yahooeng.tumblr.com/post/151148689421/open-sourcing-...</a>
So this is what Yahoo was up to for the last 10 years, instead of building any sort of security, keeping Yahoo Messenger working properly, or anything else of value? Heckuva job, Yahoo.