Today's artificial intelligence systems used for image recognition are incredibly powerful with massive potential for commercial applications. Nonetheless, current artificial neural networks--the deep learning algorithms that power image recognition--suffer one massive shortcoming: they are easily broken by images that are even slightly modified. This lack of "robustness" is a significant hurdle for researchers hoping to build better AIs. However, exactly why this phenomenon occurs, and the underlying mechanisms behind it, remain largely unknown. Aiming to one day overcome these flaws, researchers at Kyushu University's Faculty of Information Science and Electrical Engineering have published in PLOS ONE a method called "Raw Zero-Shot" that assesses how neural networks handle elements unknown to them.

In this Python tutorial, you'll learn how to facial recognition with Python and the face_recognition library Welcome to a tutorial for implementing the face recognition package for Python. The purpose of this package is to make facial recognition (identifying a face) fairly simple. Whether it's for security, smart homes, or something else entirely, the area of application for facial recognition is quite large, so let's learn how we can use this technology. To begin, we need to install everything. Installation instruction splits between Windows and Linux for some dependencies, then there is a common part for them.

I thought that the results from pix2pix by Isola et al. looked pretty cool and wanted to implement an adversarial net, so I ported the Torch code to Tensorflow. The single-file implementation is available as pix2pix-tensorflow on github. The network is composed of two main pieces, the Generator and the Discriminator. The Generator applies some transform to the input image to get the output image. The Discriminator compares the input image to an unknown image (either a target image from the dataset or an output image from the generator) and tries to guess if this was produced by the generator.