Amazing applications of Deep Learning

1. Reenacting politicians

A group from the University of Washington created a system that uses audio and synthesizes it with lip motion of a face in a video.

2. Restore colors in B&W photos and videos

Don’t like black and white images? No worries, “Let there be color!” is a computer system that can automatically restore colors in B&W photos.

3. Pixel restoration CSI style

In the show CSI they often zoom into videos beyond the resolution of the actual video. This seemed completely unreliable and there are even a few videos on YouTube like the one below where people explain they don’t watch CSI because that is unrealistic.

4. Real-time multi-person pose estimation

Deep Learning networks can now greatly aid animators in estimating the poses of people. Nowadays they can even do it in real-time. A work by Zhe Cao et al taught a neural network to estimate the position of human’s skeleton. In the video below you can see over a dozen people dancing, while the network knows where they are and how they move. This is done without having any devices on them, only by analyzing the video!

5. Describing photos

We are all used to see computers automatically classify our photos. For example, Facebook can automatically tag your friends. Similarly, Google Photos can automatically label your photos for an easier search. In fact, take a state-of-the-art network and train it on ImageNet, the biggest database of labelled image and it will be able to classify objects better than a PhD student who trained on the same task for over 100 hours.

6. Changing gazes of people in photos

This one is a little weird. Imagine you have a photo of someone, like a friend or a relative. In DeepWarp, Ganin et al trained a Deep Learning network to change the gaze of the person.

7. Real-time analysis of behaviors

So Deep Learning networks know how to recognize and describe photos and they can estimate people poses. DeepGlint is a solution that uses Deep Learning to get real-time insights about the behavior of cars, people and potentially other objects. This is an application of Deep Learning that is on the sketchy side, but it is worth being familiar with.

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