Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Ying Zheng, Sophomore Lilian Borchardt, Junior Theryn Compoc Senior Brian Danaher, Senior
Lead Presenter's Name
Brian Danaher
Faculty Mentor Name
Mihhail Berezovski
Abstract
The Signal Processing and Applied Mathematics research group at the Nevada National Security Site (NNSS) is pleased to partner with students at Embry-Riddle Aeronautical University (ERAU) to study and develop a novel pooling method for convolutional neural networks (CNN), using a publicly available data set on diabetic retinopathy images. While most image data sets perform well under existing CNN architectures, there are still challenging datasets which require further advancements, prompting us to develop new techniques for CNNs in order to satisfactorily train a network. NNSS have developed a new pooling method called "variable stride" that demonstrates advantages for networks to train faster and more efficiently. The goal of this partnership is to provide feedback and evaluation of new techniques and studying the effectiveness of this method, compared to other pooling methods.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Enhanced Deep Learning with Applications to Diabetic Retinopathy and National Security
The Signal Processing and Applied Mathematics research group at the Nevada National Security Site (NNSS) is pleased to partner with students at Embry-Riddle Aeronautical University (ERAU) to study and develop a novel pooling method for convolutional neural networks (CNN), using a publicly available data set on diabetic retinopathy images. While most image data sets perform well under existing CNN architectures, there are still challenging datasets which require further advancements, prompting us to develop new techniques for CNNs in order to satisfactorily train a network. NNSS have developed a new pooling method called "variable stride" that demonstrates advantages for networks to train faster and more efficiently. The goal of this partnership is to provide feedback and evaluation of new techniques and studying the effectiveness of this method, compared to other pooling methods.