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Modern AI for image analytics is often based on convolutional neural networks (CNNs), an infrastructure whereby networks of artificial neurons are created which broadly mimics the way the human brain learns.
Renesas also announced an enhancement to the R-Car V3M by integrating a new, highly power-efficient hardware accelerator for high-performance convolutional neural networks, which enables features such as road detection or object classification that are increasingly used in automotive applications.
Google has developed a deep convolutional neural network in the form of NIMA - Neural Image Assessment, to better rate images as per their appeal to users.
Lakhani, Paras & Sundaram, Baskaran, "Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks," Radiology.
We attempt to build a novel method of melody extraction using a convolutional neural network (CNN).
Developed over the last few years in close collaboration with leading academic institutions and pharmaceutical R&D organizations, the deep learning technology uses convolutional neural networks (CNNs) to automatically analyze microscopy images in high throughput.
Convolutional neural networks (CNNs) are neural networks that make use of the internal structure of data such as the 2D structure of image data through convolution layers [24], where each computation unit responds to a small region of input data.
a deep learning model called a convolutional neural network to process the
By utilizing the convolutional neural network (CNN) technique LG engineers are able to give the robot the ability to distinguish between a human and a dog by analyzing stored image data.
The project wants to hasten the employment of big data analytics, artificial intelligence, machine learning, computer vision and convolutional neural networks--algorithms based on biological visual cortexes--into programs of record, Shanahan said at the GEOINT Symposium in San Antonio, Texas.
Several variants of Convolutional neural network based models show that pre-trained word vectors perform better than complex deep learning models [3].
Convolutional neural networks (ConvNet) represent powerful deep learning tools for classifying different elements, by acquiring knowledge on their own from unprocessed data.