This research paper introduces DSOD, a framework that outperforms modern variations of regional proposal and classification networks.
This research shows how training low-bit deep neural networks has a much smaller memory footprint than full-precision models but with a small cost to accuracy.
Read about a mechanism to improve the memory capacity and time-scale of recurrent neural networks that allows for higher accuracy and faster training.
This robotic vision innovation uses Intel® RealSense™ technology and a Faster R-CNN to track and harvest sweet peppers as a test for helping fruit farmers.
A Fully Convolutional Model for Variable Bit Length and Lossy High-Density Compression of Mammograms
Using a convolutional autoencoder, a compression algorithm rivals JPEG and JPEG 2000 to reduce the size of mammogram images.
This paper presents a novel technique for detecting malware through classifying hardware-generated control flow traces using a deep neural network.
Get a detailed explanation of Intel® Optimization for Caffe, a deep learning framework supportive of 8-bit models to be used on Intel® Xeon® Scalable processors.
By adding multiplicative features into word-pair representations, linear and non-linear classifiers were found to have increased performance.
Bridging the gap between image segmentation and user-intended regions for applications, this method outperforms all prior approaches.
This research uses convolutional neural networks to teach machines how to convert images taken in the dark into images in full light.