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What is the simplest entry into NN image classification systems, as a C-callable library?

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The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.


Machine Learning Training Data Annotation Types for AI in News & Media

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AI in media making this industry operate with more automated tasks for better efficiency in the market. Using the computer vision or NLP/NLU, AI in news media makes the objects and languages recognition system possible for machines. Cogito provides the training data sets for AI in media and news to develop the visual perception based AI model or language based machine learning models. Media industry can well-utilize the power of face recognition system to detect the various types of faces captured into the images or videos while reporting or covering the important topics around the world. The landmark annotation technique is used to detect or recognize such faces through AI.


Impact of Covid-19 on Machine Learning in Medical Imaging Market 2020-2026

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Overview Of Machine Learning in Medical Imaging Industry 2020-2025: This has brought along several changes in This report also covers the impact …


AI Generator Learns to 'Draw' Like Cartoonist Lee Mal-Nyeon in Just 10 Hours

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A Seoul National University Master's student and developer has trained a face generating model to transfer normal face photographs into cartoon images in the distinctive style of Lee Mal-nyeon. The student (GitHub user name: bryandlee) used webcomics images by South Korean cartoonist Lee Mal-nyeon (이말년) as input data, building a dataset of malnyun cartoon faces then testing popular deep generative models on it. By combining a pretrained face generating model with special training techniques, they were able to train a generator at 256 256 resolution in just 10 hours on a single RTX 2080ti GPU, using only 500 manually annotated images. Since the cascade classifier for human faces provided in OpenCV-- a library of programming functions mainly aimed at real-time computer vision -- did not work well on the cartoon domain, the student manually annotated 500 input cartoon face images. The student incorporated FreezeD, a simple yet effective baseline for transfer learning of GANs proposed earlier this year by KAIST (Korea Advanced Institute of Science and Technology) and POSTECH ( Pohang University of Science and Technology) researchers to reduce the burden of heavy data and computational resources when training GANs. The developer tested the idea of freezing the early layers of the generator in transfer learning settings on the proposed FreezeG (freezing generator) and found that "it worked pretty well."


The Building Blocks of Artificial Intelligence

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Machine vision is the classification and tracking of real-world objects based on visual, x-ray, laser, or other signals. Optical character recognition was an early success of machine vision, but deciphering handwritten text remains a work in progress. The quality of machine vision depends on human labeling of a large quantity of reference images. The simplest way for machines to start learning is through access to this labeled data. Within the next five years, video-based computer vision will be able to recognize actions and predict motion--for example, in surveillance systems.


Disney's new AI is facial recognition for animation

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Disney's massive archive spans the course of nearly a century of content, which can turn any search for specific characters, scenes or on-screen objects within it into a significant undertaking. However, a team of researchers from Disney's Direct-to-Consumer & International Organization (DTCI) have built a machine learning platform to help automate the digital archival of all that content. They call it the Content Genome. The CG platform is built to populate knowledge graphs with content metadata, akin to what you see in Google results if you search for Steve Jobs (below). From there, AI applications can then leverage that data to enhance search, discovery and personalization features or as Anthony Accardo, Director of Research and Development at DTCI, told Engadget, help animators find specific shots and sequences from within Disney's archive.


Disney's new AI is facial recognition for animation

Engadget

Disney's massive archive spans the course of nearly a century of content, which can turn any search for specific characters, scenes or on-screen objects within it into a significant undertaking. However, a team of researchers from Disney's Direct-to-Consumer & International Organization (DTCI) have built a machine learning platform to help automate the digital archival of all that content. They call it the Content Genome. The CG platform is built to populate knowledge graphs with content metadata, akin to what you see in Google results if you search for Steve Jobs (below). From there, AI applications can then leverage that data to enhance search, discovery and personalization features or as Anthony Accardo, Director of Research and Development at DTCI, told Engadget, help animators find specific shots and sequences from within Disney's archive.


[D] Paper Explained - Deep Ensembles: A Loss Landscape Perspective (Full Video Analysis)

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Surprisingly, they outperform Bayesian Networks, which are - in theory - doing the same thing. This paper investigates how Deep Ensembles are especially suited to capturing the non-convex loss landscape of neural networks.



Disney's Developed Movie-Quality Face-Swapping Technology That Promises to Change Filmmaking

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In a few short years, neural-network-powered automated face swaps have gone from being mildly convincing to eerily believable. But through new research from Disney, neural face-swapping is poised to become a legitimate and high-quality tool for visual effects studios working on Hollywood blockbusters. One of the bigger challenges of creating deepfake videos, as they've come to be known, is creating a vast database of facial images of a person--thousands of different expressions and poses--that can be swapped into a target video. The larger the database and the higher the quality of the images, the better the face swaps will turn out. But the images (which are more often than not headshots of famous people) are usually pulled from sources with limited resolution.