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GTC ON-DEMAND

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Abstract:
Learn how the combination of capsule networks, active learning, and transfer learning can reduce the number of training samples required to add a new label to an existing classifier. We will detail how we developed our network architecture, training data selection algorithm, and discuss their implementation in Python and TensorFlow's Keras layers with GPU acceleration. We'll also discuss our results from applying this approach to image-classification tasks and how they compare to a standard convolutional neural network approach.
Learn how the combination of capsule networks, active learning, and transfer learning can reduce the number of training samples required to add a new label to an existing classifier. We will detail how we developed our network architecture, training data selection algorithm, and discuss their implementation in Python and TensorFlow's Keras layers with GPU acceleration. We'll also discuss our results from applying this approach to image-classification tasks and how they compare to a standard convolutional neural network approach.  Back
 
Topics:
Advanced AI Learning Techniques, AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9290
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