adversarial nets (deep learning)
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been...
I bet most of us have seen a lot of AI-generated people faces in recent times, be it in papers or blogs. We have reached a stage where it is becoming increasingly difficult to distinguish between actual human faces and faces that are generated by Artificial Intelligence. In this post, I will help the reader to understand how they can create and build such applications on their own. I will try to keep this post as intuitive as possible for starters while not dumbing it down too much. This post is about understanding how GANs work.
A blog about Compressive Sensing, Computational Imaging, Machine Learning. Using priors to avoid the curse of dimensionality arising in Big Data.
What we'd like to find out about GANs that we don't know yet.
Emil Mikhailov is the founder of XIX.ai [http://XIX.ai] (YC W17). Roman Trusov is a researcher at XIX.ai. Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you want. This fact steadily becomes worrisome as more and more systems are powered by artificial intelligence — and many of them are crucial for our safe and comfortable life. Banks, sur