dear fellow scholars this is two minute
papers with dr. Khurana if I hear when
we humans look at an image or a piece of
video footage such as this one we all
understand that this is just a 2d
projection of the world around us so
much so that if we have the time and
patience we could draw the depth map
that describes the distance of each
object from the camera this information
is highly useful because we can use it
to create real-time defocus effects for
virtual reality and computer games
or even perform this Ken Burns effect in
3d or in other words zoom and pan around
in a photograph but with a beautiful
twist because in the meantime we can
reveal the depth of the image however
when we show the same images to a
machine all it sees is a bunch of
numbers fortunately with the ascendancy
of neural network based learning
algorithms we now have a chance to do
this reasonably well for instance who
discussed this depth perception neural
network in an earlier episode which was
trained using a large number of
input-output pairs where the inputs are
a bunch of images and the outputs are
their corresponding depth maps for the
neural network to learn from the authors
implemented this with a random scene
generator which creates a bunch of these
crazy configurations with a lot of
occlusions and computes via simulation
the appropriate depth map for them this
is what we call supervised learning
because we have all these input-output
pairs the solutions are given in the
training set to guide the training of
the neural network this is supervised
learning machine learning with crutches
we can also use this depth information
to enhance the perception of
self-driving cars but this application
is not like the previous two I just
mentioned it is much much harder because
in the earlier supervised learning
example we have trained a neural network
in a simulation and then we also use it
later in a computer game which is of
course another simulation we control all
the variables and the environment here
however self-driving cars need to be
deployed in the real world these cars
also generate a lot of video footage
with their sensors which could be fed
back to the neural networks as
additional training data if we had the
deaf maps for them which of course
unfortunately we don't and now with this
we have arrived to the concept of
unsupervised learning unsupervised
learning is proper machine learning
where no crutches are allowed we just
unleash the algorithm on a bunch of data
with no labels and if we do it well the
neural network will learn
something useful from it it is very
convenient because any video we have may
be used as training data that would be
great but we have a tiny problem and
that tiny problem is that this sounds
impossible or it may have sounded
impossible until this paper appeared
this work promises us no less than
unsupervised depth learning from videos
since this is unsupervised it means that
during training all it sees is unlabeled
videos from different viewpoints and
somehow figures out a way to create
these depth maps from it so how is this
even possible
well it is possible by adding just one
ingenious idea the idea is that since we
don't have the labels we can teach the
algorithm how to be right but instead we
can teach it to be consistent that
doesn't sound like much does it
well it makes all the difference because
if we ask the algorithm to be consistent
it will find out that the good way to be
consistent is to be right while we are
looking at some results to make this
clearer let me add one more real-world
example that demonstrates how cool this
idea is imagine that you are a
university professor overseeing an exam
in mathematics and someone tells you
that for one of the problems most of the
students give the same answer if this is
the case there is a good chance that
this was the right answer it is not a
hundred percent chance that this is the
case but if most of the students have
the same answer it is much more unlikely
that they have all failed the same way
there are many different ways to fail
but there is only one way to succeed
therefore if there is consistency often
there is success and this simple but
powerful thought leads to far reaching
conclusions
let's have a look at some more results
whoo-hoo now this is something let me
explain why I am so excited for this
this is the input image and this is the
perfect depth map that is concealed from
our beloved algorithm and is there for
us to be able to evaluate its
performance these are two previous works
both used crutches
the first was trained via supervised
learning by showing it input-output
image pairs with depth maps and it does
reasonably well while the other one gets
even less supervision the worse crutch
if you will and it came up with this now
the unsupervised new technique was not
given any crutches and came up with this
holy mother of papers it looks like a
somewhat coarser but still very accurate
version of the true depth maps so what
do you know this neural network based
method looks at unlabeled videos and
finds a way to create depth maps by not
trying to be right but trying to be
consistent this is one of those amazing
papers where one simple brilliant idea
can change everything and make the
impossible possible what a time to be
alive
what you see here is an instrumentation
of this depth learning paper we have
talked about this was made by weights
and biases
I think organizing these experiments
really showcases the usability of their
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it is really as good as it gets make
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thanks for watching and for your
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time