Xander Steenbrugge shared his latest work on LinkedIn yesterday, and I was completely stunned!
Xander had been working on, what he called, a “fun side-project”, but which was in my eyes, absolutely awesome. He had used two generative adversarial networks (GANs) to teach one another how to respond visually to changing audio cues.
This resulted in the generation of stunning audio-visual fanatasy worlds that are complete brain porn. You just can’t stop staring. So much is happening in these video’s; everything looks familiar, whereas nothing really represent anything realistic. There’s always a sliver of reality before the visual shapes morph to their next form.
Have a look yourself at the video’s on Xander’s new Youtube channel “Neural Synesthesia“ dedicated to this project. The videos are also hosted here on Vimeo, where they are rendered in higher resolution even.
This is my favorite video, but there are more below.
Amazing how the image responds to changes in the music, right? I suspect Xander let’s the algorithm traverse some latent space with spaces that are determined by the bass, trebble, and other audio-cues.
Here’s another one of Xander’s videos, with the same audio track as background:
But Xander didn’t limit his GANs to generating landscapes and still paintings, but he also dared to do some human faces. These also turned out amazing.
Both the left and right face seem to start out in about the same position/seed in the latent space, but traverse in different, though still similar directions, morphing into all kinds of reaslistic and more alien forms. The result is simply out of this world!
Curious to see where this project and others head as we continue to see development in this GAN field. This must turn the world of design and art up side down in the coming decade…
A beautiful machine-generated still from the Neural Synthesia videos (link)
Adam Geitgey likes to write about computers and machine learning. He explains machine learning as “generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.” (Part 1)
Adam’s visual explanation of two machine learning applications (original from Part 1)
In the fourth part of his series on machine learning Adam touches on Facial Recognition. Facebook is one of the companies using such algorithms in real-time, allowing them to recognize your friends’ faces after you’ve tagged them only a few times. Facebook reports they recognize faces with 97% accuracy, which is comparable to our own, human facial recognition abilities!
Facebook’s algorithms recognizing and automatically tagging Adam’s family. Helpful or creepy? (original from Part 4)
Adam decided to put up a challenge: would a facial recognition algorithm be able to distinguish Will Ferrell (famous actor) from Chad Smith (famous rock musician)? Indeed, these two celebrities look very much alike:
If you want to train such an algorithm, Adam explain, you need to overcome a series of related problems:
First, look at a picture and find all the faces in it
Second, focus on each face and be able to understand that even if a face is turned in a weird direction or in bad lighting, it is still the same person.
Third, be able to pick out unique features of the face that you can use to tell it apart from other people— like how big the eyes are, how long the face is, etc.
Finally, compare the unique features of that face to all the people you already know to determine the person’s name.
How the facial recognition algorithm steps might work (original from Part 4)
To detect the faces, Adam used Histograms of Oriented Gradients (HOG). All input pictures were converted to black and white (because color is not needed) and then every single pixel in our image is examined, one at a time. Moreover, for every pixel, the algorithm examined the pixels directly surrounding it:
Illustration of the algorithm as it would take in a black and white photo of Will Ferrel (original from Part 4)
The algorithm then checks, for every pixel, in which direction the picture is getting darker and draws an arrow (a gradient) in that direction.
Illustration of how algorithm would reduce a black and white photo of Will Ferrel to gradients (original from Part 4)
However, to do this for every single pixel would require too much processing power, so Adam broke up pictures in 16 by 16 pixel squares. The result is a very simple representation that does capture the basic structure of the original face, based on which we can now spot faces in pictures. Moreover, because we used gradients, the result will be similar regardless of the lighting of the picture.
The original image turned into a HOG representation (original from Part 4)
Now that the computer can spot faces, we need to make sure that it knows that two perspectives of the same face represent the same person. Adam uses landmarks for this: 68 specific points that exist on every face. An algorithm can then be trained to find these points on any face:
The 68 points on the image of Will Ferrell (original from Part 4)
Now the computer knows where the chin, the mouth and the eyes are, the image can be scaled and rotated to center it as best as possible:
The image of Will Ferrell transformed (original from Part 4)
Adam trained a Deep Convolutional Neural Network to generate 128 measurements for each face that best distinguish it from faces of other people. This network needs to train for several hours, going through thousands and thousands of face pictures. If you want to try this step yourself, Adam explains how to run OpenFace’s lua script. This study at Google provides more details, but it basically looks like this:
The training process visualized (original from Part 4)
After hours of training, the neural net will output 128 numbers accurately representing the specific face put in. Now, all you need to do is check which face in your database is most closely resembled by those 128 numbers, and you have your match! Many algorithms can do this final check, and Adam trained a simple linear SVM classifier on twenty pictures of Chad Smith, Will Ferrel, and Jimmy Falon (the host of a talkshow they both visited).
In the end, Adam’s machine had learned to distinguish these three people – two of whom are nearly indistinguishable with the human eye – in real-time:
Adam Geitgey’s facial recognition algorithm in action: providing real time classifications of the faces of lookalikes Chad Smith and Will Ferrel at Jimmy Falon’s talk show (original from Part 4)
Many requests have come in regarding “training datasets” – to practice programming. Fortunately, the internet is full of open-source datasets! I compiled a selected list of datasets and repositories below. If you have any additions, please comment or contact me! For information on programming languages or algorithms, visit the overviews for R, Python, SQL, or Data Science, Machine Learning, & Statistics resources.
This list is no longer being maintained. There are other, more frequently updated repositories of useful datasets included in bold below:
