Comparative Study of Deep Learning Parameter Selection for Multi-Output Regression on Head Pose Estimation

Engineering

• Loss Function 100%
• Head Pose Estimation 100%
• Batch Size 100%
• Deep Learning Method 100%
• Complex Model 75%
• Mean Absolute Error 50%
• Optimal Performance 50%
• Problem Domain 50%
• Comparative Analysis 50%
• Image Classification 25%
• Gradient Descent 25%
• Classification Problem 25%
• Convolutional Neural Network 25%

Mathematics

• Regression Output 100%
• Loss Function 100%
• Batch Size 100%
• Deep Learning Method 100%
• Complex Model 75%
• Absolute Error 50%
• Problem Domain 50%
• Regression Model 25%
• Stochastics 25%
• Squared Error 25%
• Classification Problem 25%
• Performance Model 25%
• Convolutional Neural Network 25%

Chemical Engineering

• Neural Network 100%
• Deep Learning Method 100%