Neural Network Generalization via Physical Simulation Parameter Control
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Solution Overview
Problem
Existing methods for training neural networks struggle to adapt to real-world changes, leading to difficulties in generalization performance, particularly in environments like automated driving where accurate recognition of vehicles and pedestrians is crucial.
Innovation Solution
An information processing device that integrates a physical simulator to generate images conforming to real-world environments, allowing for efficient machine learning by transmitting image information and parameters between a control unit, a physical simulator, and a machine learning unit, enabling improved generalization performance through dynamic parameter setting and image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network is trained using conventional methods, then training can be completed with existing data, but the network fails to generalize well to real-world changes and environmental variations
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network using a physical simulator before deploying it to real-world environments. The simulator generates synthetic training data in advance, allowing the network to learn fundamental patterns and physics-based relationships before encountering actual real-world variations, thereby improving generalization performance.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying simulation parameters (such as lighting conditions, object positions, camera angles, and environmental factors) to generate diverse training scenarios. This exposes the neural network to a wide range of potential real-world conditions, enhancing its adaptability and generalization capability without requiring extensive real-world data collection.
2Reliability
If diverse real-world training data is collected to improve generalization, then adaptability increases, but data collection time and computational resources increase significantly
Solution Approach 1:
The patent applies copying by creating synthetic copies of real-world scenarios through physical simulation. Instead of collecting diverse real-world data, the system generates synthetic training data that replicates real-world physics and environmental conditions, significantly reducing data collection time while maintaining generalization performance.
Solution Approach 2:
The patent uses preliminary action by pre-generating comprehensive training datasets through simulation before actual deployment. This allows extensive data preparation and network training to be completed in advance using computational resources, rather than requiring time-consuming real-world data collection and processing during deployment phases.
3Productivity
If the neural network is trained with simulated data only, then training efficiency improves, but the network lacks exposure to real-world complexity and variations
Solution Approach 1:
The patent applies preliminary action by using simulated data for initial network training to establish fundamental recognition capabilities efficiently. This preliminary training phase provides a strong foundation that can then be refined with real-world data if needed, balancing training efficiency with real-world accuracy.
Solution Approach 2:
The patent uses parameter changes in the physical simulator to generate training data that progressively increases in complexity and realism. By systematically varying simulation parameters to match real-world conditions more closely, the network is exposed to increasing levels of real-world complexity while maintaining the efficiency benefits of simulated training.
Data Source
AI summary
There is provided an information processing device to improve generalization performance of the neural network, the information processing device including: a control unit configured to control display related to a setting of a parameter related to physical simulation; a communication unit configured to transmit the parameter to a physical simulator and receive image information obtained in the physical simulation from the physical simulator; and a machine learning unit configured to perform machine learning on the basis of the image information. The control unit causes a display unit to display a learning result obtained by the machine learning unit and the parameter in association with each other.