Search Results for author:
Found 9 papers, 3 papers with code
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
Findings: The COVID-Net S deep neural networks yielded R$^2$ of 0. 664 $\pm$ 0. 032 and 0. 635 $\pm$ 0. 044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments.
PuckNet: Estimating hockey puck location from broadcast video
The network is able to regress the puck location from broadcast hockey video clips with varying camera angles.
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms
A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of ImageNet for the task of image classification.
State of Compact Architecture Search For Deep Neural Networks
A particularly interesting class of compact architecture search algorithms are those that are guided by baseline network architectures.
Squeeze-and-Attention Networks for Semantic Segmentation
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features.
Ranked #6 on
Semantic Segmentation
on PASCAL VOC 2012 test
EdgeSegNet: A Compact Network for Semantic Segmentation
In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation.
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance-level object segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network.
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devices.
