Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér.
This is the third episode in our series of
Deep Learning applications. I have mixed in
some recurrent neural networks for your, and
honestly, my own enjoyment.
I think this series of applications shows
what an amazingly versatile tool we have been
blessed with with deep learning. And I know
you Fellow Scholars have been quite excited
for this one! Let's get started!
This piece of work accomplishes geolocation
for photographs. This means that we toss in
a photograph, and it tells us exactly where
it was made.
Super resolution is a hot topic where we show
a coarse, heavily pixelated image to a system,
and it tries to guess what it depicts and
increase the resolution of it. If we have
a tool that accomplishes this, we can zoom
into images way more than the number of megapixels
of our camera would allow. It is really cool
to see that deep learning has also made an
appearance in this subfield.
This handy little tool visualizes the learning
process in a neural network with the classical
forward and backward propagation steps.
This recurrent neural network continues our
sentences in a way that kind of makes sense.
Well, kind of.
Human in the loop techniques seek to create
a bidirectional connection between humans
and machine learning techniques so they can
both learn from each other. I think it definitely
is an interesting direction - at first, DeepMind's
AlphaGo also learned the basics of Go from
amateurs and then took off like a hermit to
learn on its own and came back with guns blazing.
We usually have at least one remarkably rigorous
and scientific application of deep learning
in every collection episode. This time, I'd
like to show you this marvelous little program
that suggests emojis for your images. It does
so well, that nowadays, even computer algorithms
are more hip than I am.
This application is akin to the previous one
we have seen about super resolution - here,
we see beautiful, high resolution images of
digits created from these tiny, extremely
pixelated inputs.
Netflix is an online video streaming service.
The Netflix Prize was a competition
where participants wrote programs to estimate
how a user would enjoy a given set of movies
based on this user's previous preferences.
The competition was won by an ensemble algorithm,
which is essentially a mixture of many existing
techniques. And by many, I mean 107. It is
not a surprise that some contemptuously use
the term 'abomination' instead of 'ensemble'
because of their egregious complexity.
In this blog post, a simple neural network
implementation is described that achieves
quite decent results and the core of the solution
fits in no more than 20 lines of code.
The code has been written using Keras, which
also happens to be one of my favorite deep
learning libraries. Wholeheartedly recommended
for everyone who likes to code, and a big
shoutout to Francois, the developer of the
mentioned library. Amazing feat.
Convolutional Neural Networks have also started
curating works of art by assigning a score
to how aesthetic they are.
Oh, sorry Leonardo!
Earlier we talked about adversarial techniques
that add a very specific type of noise to
images to completely destroy the accuracy
of previously existing image classification
programs. The arms race has officially started,
and new techniques are popping up to prevent
this behavior.
If you find some novel applications of deep
learning, just send a link my way in the comments
section.
Thanks for watching, and for your generous
support, and I'll see you next time!
