Gradient driven learning for pooling in visual pipeline feature extraction models

Engineering

• Feature Extraction 100%
• Pattern Recognition 50%
• Design Choice 50%
• Classification Accuracy 50%
• Gradient Descent 50%
• Potential Gain 50%
• Error Signal 50%

Computer Science

• Feature Extraction 100%
• Gradient Descent 50%
• Representation Space 50%
• Potential Gain 50%
• Classification Accuracy 50%
• Pattern Recognition 50%

Neuroscience

• Pattern Recognition 100%