Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution

#artificialintelligence

In this tutorial, we will learn how to use deep learning to compose images in the style of another image (ever wish you could paint like Picasso or Van Gogh?). This is known as neural style transfer! This is a technique outlined in Leon A. Gatys' paper, A Neural Algorithm of Artistic Style, which is a great read, and you should definitely check it out. Neural style transfer is an optimization technique used to take three images, a content image, a style reference image (such as an artwork by a famous painter), and the input image you want to style -- and blend them together such that the input image is transformed to look like the content image, but "painted" in the style of the style image. For example, let's take an image of this turtle and Katsushika Hokusai's The Great Wave off Kanagawa: Now how would it look like if Hokusai decided to add the texture or style of his waves to the image of the turtle?


Has AI Gone Too Far? - Automated Inference of Criminality Using Face Images

@machinelearnbot

Summary: This new study claims to be able to identify criminals based on their facial characteristics. Even if the data science is good has AI pushed too far into areas of societal taboos? This isn't the first time data science has been restricted in favor of social goals, but this study may be a trip wire that starts a long and difficult discussion about the role of AI. Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ.


Has AI Gone Too Far? - Automated Inference of Criminality Using Face Images

@machinelearnbot

Summary: This new study claims to be able to identify criminals based on their facial characteristics. Even if the data science is good has AI pushed too far into areas of societal taboos? This isn't the first time data science has been restricted in favor of social goals, but this study may be a trip wire that starts a long and difficult discussion about the role of AI. Has AI gone too far? This might seem like a nonsensical question to data scientists who strive every day to expand the capabilities of AI until you read the headlines created by this just released peer reviewed scientific paper: Automated Inference on Criminality Using Face Images (Xiaolin Wu, McMaster Univ.


A Scalable Approach to Column-Based Low-Rank Matrix Approximation

AAAI Conferences

In this paper, we address the column-based low-rank matrix approximation problem using a novel parallel approach. Our approach is based on the divide-and-combine idea. We first perform column selection on submatrices of an original data matrix in parallel, and then combine the selected columns into the final output. Our approach enjoys a theoretical relative-error upper bound. In addition, our column-based low-rank approximation partitions data in a deterministic way and makes no assumptions about matrix coherence. Compared with other traditional methods, our approach is scalable on large-scale matrices. Finally, experiments on both simulated and real world data show that our approach is both efficient and effective.


Recognizing Artificial Faces Using Wavelet Based Adapted Median Binary Patterns

AAAI Conferences

Recognizing avatar faces is a challenge and very important issue for terrorism and security experts. Recently some avatar face recognition techniques are proposed but they are still limited. In this paper, we propose a novel face recognition technique based on discrete wavelet transform and Adapted Median Binary Pattern (AMBP) operator to recognize avatar faces from different virtual worlds. The original LBP operator mainly thresholds pixels in a specific predetermined window based on the central pixel’s value of that window. As a result the LBP operator becomes more sensitive to noise especially in near-uniform or flat area regions of an image. One way to reduce the effect of noise is to update the threshold automatically based on all pixels in the neighborhood using some simple statistical operations. Experiments conducted on two virtual world avatar face image datasets show that our technique performs better than original LBP, adapted LBP, Median Binary Pattern (MBP) and wavelet statistical adapted LBP in terms of accuracy.