Zedge summer interns developed a very cool app using ARKit and CoreML (on iOS11). As parts of their journey the published 2 blog posts on the Zedge corporate web site related to: How to develop and run Generative Adversarial Networks (GAN) for Creative AI on the iPhone using Apple’s CoreML tools, check out their blog ..read more

Making Self-driving cars work requires several technologies and methods to pull in the same direction (e.g. Radar/Lidar, Camera, Control Theory and Deep Learning). The online available Self-Driving Car Nanodegree from Udacity (divided into 3 terms) is probably the best way to learn more about the topic (see [Term 1], [Term 2] and [Term 3] for ..read more

Magnetic Resonance Imaging (MRI) can be used in many types of diagnosis e.g. cancer, alzheimer, cardiac and muscle/skeleton issues. This blog post has recent publications of Deep Learning applied to MRI (health-related) data, e.g. for segmentation, detection, demonising and classification. MRI is described in Wikipedia as: Magnetic resonance imaging (MRI) is a medical imaging technique ..read more

Scaling down images is a craft, scaling up images is an art Since in the scaling down to a lower resolution you typically need to remove pixels, but in the case of scaling up you need to invent new pixels. But some Deep Learning models with Convolutional Neural Networks (and frequently Deconvolutional layers) has shown successful to scale up ..read more

Traffic Sign Detection and Recognition is key functionality for self-driving cars. This posting has recent papers in this area. Check also out related posting: Deep Learning for Vehicle Detection and Classification Best regards, Amund Tveit Amund Tveit Year  Title Author 2016   Road surface traffic sign detection with hybrid region proposal and fast R-CNN  R ..read more

Update: 2017-Feb-03 – launched new service – ai.amundtveit.com (navigation and search in papers). Try e.g. out its Vehicle, Car and Driving pages. This posting has recent papers about vehicle (e.g. car) detection and classification, e.g. for selv-driving/autonomous cars. Related: check also out Nvidia‘s End-to-End Deep Learning for Self-driving Cars and Udacity‘s Self-Driving Car Engineer (Nanodegree). Best ..read more

Gaussian Process is a statistical model where observations are in the continuous domain, to learn more check out a tutorial on gaussian process (by Univ.of Cambridge’s Zoubin G.). Gaussian Process is an infinite-dimensional generalization of multivariate normal distributions. Researchers from University of Sheffield – Andreas C. Damanianou and Neil D. Lawrence – started using Gaussian ..read more

Ultrasound (also called Sonography) are sound waves with higher frequency than humans can hear, they frequently used in medical settings, e.g. for checking that pregnancy is going well with fetal ultrasound. For more about Ultrasound data formats check out Ultrasound Research Interface. This blog post has recent publications about applying Deep Learning for analyzing Ultrasound ..read more

This chapter presents recent papers for using FPGAs (Field Programmable Gate Arrays) for Deep Learning. FPGAs can roughly be seen as a Software-configurable Hardware, i.e you in some cases get close to dedicated hardware speed (although typically at lower clock frequency than chips, but typically with strong on-FPGA parallelism), this can be a potential good ..read more

Zero-Shot Learning is making decisions after seing only one or few examples (as opposed to other types of learning that typically requires large amount of training examples). Recommend having a look at An embarrassingly simple approach to zero-shot learning first. Best regards, Amund Tveit Less is more: zero-shot learning from online textual documents with noise suppression ..read more