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GTC ON-DEMAND

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Abstract:
Learn how synthetic data can be used to develop traditional and Convolutional Neural Network (CNN) image segmentation models when labelled training data is limited. We will describe hard drive media defect patterns and how they relate to problems in the manufacturing line, show why CNN models were chosen for some defect patterns, and how the CNN models were trained using both synthetic and real data. Different architectures using CNNs were explored and the resulting benefits and drawbacks are presented.
Learn how synthetic data can be used to develop traditional and Convolutional Neural Network (CNN) image segmentation models when labelled training data is limited. We will describe hard drive media defect patterns and how they relate to problems in the manufacturing line, show why CNN models were chosen for some defect patterns, and how the CNN models were trained using both synthetic and real data. Different architectures using CNNs were explored and the resulting benefits and drawbacks are presented.  Back
 
Topics:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8415
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