Multitask pattern recognition for autonomous robots

We present a method called multitask pattern recognition (MTPR) that improves the accuracy and robustness of neural net based vision systems. The method trains neural nets on auxiliary recognition problems at the same time as the nets are trained on the main recognition task. The predictions the system makes for the auxiliary recognition tasks are not used, but the internal features learned for the auxiliary tasks improve performance on the main task. In addition, the auxiliary tasks allow us to focus the attention of learning towards image features it would otherwise ignore, thereby increasing the robustness of the learned models. We demonstrate MTPR on three problems (one synthetic, two real). On these problems MTPR improves performance 10%-30%. MTPR is applicable to many pattern recognition problems.

Authors

• Rich Caruana
• Joseph O'Sullivan

Keywords

• Pattern recognition
• Robots
• Neural networks
• Robustness
• Road vehicles
• Machine vision
• Focusing
• Learning systems
• Decision trees
• Nearest neighbor searches
• Recognition Task
• Neural Net
• Pattern Recognition Problems
• Learning Rate
• Autonomic System
• Hidden Layer
• Road Surface
• Camera Images
• Early Stopping
• Training Tasks
• Additional Tasks
• Left Edge
• Long-term Prediction
• Distance Scale
• Training Signal
• Horizontal Stripes
• Real Domain
• Lane Markings
• Pattern Recognition Tasks
• Extra Tasks