Marking artifacts in images using machine learning
Advisor(s)
Dr. Chihway Chang, University of Chicago
Location
Room A119
Start Date
26-4-2019 10:05 AM
End Date
26-4-2019 10:20 AM
Abstract
With the utilization of machine learning, computers have been able to efficiently classify data. Deep learning, a division of machine learning, uses multiple nonlinear processing units. Convolutional neural networks are a machine learning model patterned after the structure of an animal’s visual cortex. In our project, deep learning and convolutional neural networks were both used to distinguish cosmic rays, artifacts characterized by high-energy radiation, in images taken by the National Optical Astronomy Observatory’s telescopes. Once we had constructed a deep convolutional neural network, this network was trained and fine-tuned continuously until its classifications reached maximum accuracy.
Marking artifacts in images using machine learning
Room A119
With the utilization of machine learning, computers have been able to efficiently classify data. Deep learning, a division of machine learning, uses multiple nonlinear processing units. Convolutional neural networks are a machine learning model patterned after the structure of an animal’s visual cortex. In our project, deep learning and convolutional neural networks were both used to distinguish cosmic rays, artifacts characterized by high-energy radiation, in images taken by the National Optical Astronomy Observatory’s telescopes. Once we had constructed a deep convolutional neural network, this network was trained and fine-tuned continuously until its classifications reached maximum accuracy.