Color transfer, Image editing and Automatic translation

As a follow-up of my previous introductory article on optimal transport and a first part of this guide provided by Aurelie Boisbunon here, I will present below how you can solve different tasks with Optimal Transport (OT) in practice using the Python Optimal Transport (POT) toolbox.

To start with, let us install POT using pip from the terminal by simply running

pip3 install ot

And voilà! If everything went well, you now have POT installed and ready to use on your computer. Let me now explain how you can reproduce the results from my previous article.

Color transfer

In this application our goal is to transfer the color style of one image onto another image in the smoothest way possible. To do this, we will follow the example from the official webpage of the POT library and start by defining several supplementary functions needed when working with…

30,000kms on the thumb around the globe in 1000 words

In the last 8 years of my life, I hitchhiked in almost 30 different countries with a total of around 30,000kms traveled. While I do not hitchhike that much nowadays, I feel that the experiences gained on the road learned me a lot about life and people’s behavior in general, and sharing the lessons I have learned from them with you is what I would like to do below.

Lesson #1: Create your chances

Getting lucky is a matter of you doing something for it to happen.

I learned this one from all the times when I got dropped off in places from which it seemed impossible to get a ride. I was standing on the side of the road watching all the cars passing by so fast they couldn’t probably even see me, and understanding that I needed to get extremely lucky to get out of there. You know what my solution was in these situations? I usually chose whatever direction felt safe, put my backpack on the shoulders, and started advancing in that direction to improve my current position rather than staying immobile and waiting for something to happen. I do not know why and how this works exactly, but cars that some minutes ago were speeding away now were making their best to pull over safely to pick me up. When asked why they stopped, the drivers usually replied “Oh, I just saw you walking.”
My hint about this one is that people tend to have more sympathy when they see that you are trying to find a solution, and not just hoping for it to come as a blessing or a stroke of good luck. Every time you are cornered by the unfavorable circumstances, try to seek this little tiny improvement, this extra 1% increasing your chances to get out of the situation you are in. Somehow, this one extra percent is often exactly what you need to succeed, whether it will be on your own or with the help of somebody else. …

Explaining one of the most emerging methods in machine learning right now

Would you believe me if I were to say that there is a single solution to such different problems as brain decoding in neuroscience, shape reconstruction in computer graphics, color transfer in computer vision, and automatic translation in neural language processing? And if I were to add transfer learning, image registration, and spectral unmixing for musical data to this list and the fact that the solution I am talking about has nothing to do with deep learning? …

An (almost) math-free guide to understanding the theory behind transfer learning and domain adaptation.

During the NIPS tutorial talk given in 2016, Andrew Ng said that transfer learning — a subarea of machine learning where the model is learned and then deployed in related, yet different, areas — will be the next driver of machine learning commercial success in the years to come. This statement would be hard to contest as avoiding learning large-scale models from scratch would significantly reduce the high computational and annotation efforts required for it and save data science practitioners lots of time, energy, and, ultimately, money.

As an illustration of these latter words, consider Facebook’s DeepFace algorithm that was the first to achieve a near-human performance in face verification back in 2014. The neural network behind it was trained on 4.4 million labeled faces — an overwhelming amount of data that had to be collected, annotated, and then trained on for 3 full days without taking into account the time needed for fine-tuning. It won’t be an exaggeration to say that most of the companies and research teams without Facebook’s resources and deep learning engineers would have to put in months or even years of work to complete such a feat, with most of this time spent on collecting an annotated sample large enough to build such an accurate classifier. …