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Top /r/MachineLearning Posts, March: Hugs, Deep Learning Navigation, 3D Face Capture, AlphaGo!


What's huggable, adversarial images for deep learning, overview of real-time 3D face capture and reenactment, deep learning quadcopter navigation, and a whole lot of AlphaGo!



In March on /r/MachineLearning, we learn what's huggable, find adversarial images for deep learning, get an overview of real-time 3D face capture and reenactment, see deep learning navigate a quadcopter, and AlphaGo, AlphaGo, AlphaGo!

Note: given the high number of top-ranking AlphaGo-related posts in /r/MachineLearning this month (and for good reason), a number of the highest performing posts will be consolidated below in order to avoid repetition.

The top 5 /r/MachineLearning posts of the past month are:

1. Can I Hug That? I trained a classifier to tell you whether or not what's in an image is huggable. +586

This is a link to some resulting images of a classifier trained to determined whether a given photo is of something deemed huggable, along with its huggability score. The comments include some insight into the classifier's construction, along with some other entertaining discussion.

Huggable

2. AlphaGO WINS! +578

This is the first of the AlphaGo posts, started way back when AlphaGo had just beaten Lee Sedol in their very first match. The post is a discussion of what did, and would, happen, and in its already-historical context is somewhat entertaining. Here are a few more related posts, for those interested in AlphaGo's recent exploits:

3. Adversarial images for deep learning +456

This is a link to the now-classic 'Chihuahua or muffin?' image, the best adversarial example image this month besides 'Pug or bread?'. The comments are here, with a a host of additional adversarial images linked within.

4. Face2Face: Real-time Face Capture and Reenactment of RGB Videos (CVPR 2016 Oral) +425

This is a link to a very cool video demonstrating a computer vision system outlined here. From the authors:

Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure.

The video is only a few minutes long and definitely worth your time, if even for a high-level overview.

5. Quadcopter Navigation in the Forest using Deep Neural Networks +357

This is a link to another video, this time of a quadcopter navigating through a forest via deep learning, just as the title suggests. The video is only 5 minutes long, but is really much more than a simple system demo, as it provides insight into how the classifier was trained, and problems encountered, and their solutions, during development. Catch the discussion here.

And here's a parting image, from a latecomer post from the month, which came in too late to reach the top. However, it was posted with the titled, "This xkcd was released less than 2 years ago," and is particularly relevant to recent developments.


xkcd comic

Here is the link to the original comic. The discussion is worth a look as well.

Related:


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