A Primer in Adversarial Machine Learning – The Next Advance in AI


Even more concerning, researchers have shown that completely random nonsense images can be misclassified by CNNs with very high confidence as objects recognizable to humans, even though a human would clearly recognize that there was no image there at all (e.g. If those system observations are intentionally tainted with noise designed to defeat the CNN recognition, the system will be trained to make incorrect conclusions about whether a malevolent intrusion is occurring. Adversarial Machine Learning is an emerging area in deep neural net (DNN) research. The current state of AI has advanced to general image, text, and speech recognition, and tasks like steering the car or winning a game of chess.

The Strange Loop in Deep Learning – Intuition Machine – Medium


My first recollection of an effective Deep Learning system that used feedback loops where in "Ladder Networks". In an architecture developed by Stanford called "Feedback Networks", the researchers explored a different kind of network that feeds back into itself and develops the internal representation incrementally: In an even more recently published research (March 2017) from UC Berkeley have created astonishingly capable image to image translations using GANs and a novel kind of regularization. The major difficulty of training Deep Learning systems has been the lack of labeled data. So the next time you see some mind boggling Deep Learning results, seek to find the strange loops that are embedded in the method.

Neural Face ?? ??


Generator and Discriminator consist of Deconvolutional Network (DNN) and Convolutional Neural Network (CNN). CNN is a neural network which encodes the hundreds of pixels of an image into a vector of small dimensions (z) which is a summary of the image. When a real image is given, Discriminator should output 1 or 0 for whether the image was generated from Generator. In the contrast, Generator generates an image from z, which follows a Gaussian Distribution, and tries to figure out the distribution of human images from z.