Training Data Transformer for Neural Network Simulation
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Solution Overview
Problem
The challenge lies in the gap between synthesized training data and real-world data for autonomous devices, as real-world data contains inaccuracies and non-random noises that are difficult to model, leading to ineffective training of neural networks in virtual environments for real-world applications.
Innovation Solution
A constrained generative adversarial network is used to transform simulated training data into more representative real-world data by incorporating random noise and a distortion loss function, ensuring the generated data conforms to both simulated and real-world sensory and input device signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synthesized training data is used for neural network training, then training efficiency and data availability are improved, but the accuracy and reliability of training are worsened due to the gap between synthetic and real-world data characteristics
Solution Approach 1:
A training data transformer is introduced as an intermediary component that receives synthesized training data and transforms it into transformed training data that better conforms to real-world data characteristics. This mediator bridges the gap between synthetic data (which is easy to generate) and real-world data (which is difficult to obtain), enabling neural networks to be trained efficiently while maintaining training accuracy and reliability for real-world applications.
2Reliability
If real-world training data is collected, then training accuracy and reliability are improved, but data collection complexity and cost increase
Solution Approach 1:
Instead of directly collecting real-world training data, the system creates synthetic training data that copies and mimics the essential characteristics of real-world data through virtual environments and simulations. This approach preserves the training accuracy and reliability that would otherwise require complex real-world data collection, while significantly reducing the complexity and cost of data acquisition.
3Loss of time
If virtual environment training is used, then training cost and time are reduced, but the effectiveness of training for real-world applications is worsened due to data gap
Solution Approach 1:
The training data transformer incorporates feedback mechanisms that continuously monitor and adjust the transformed training data to better match real-world data characteristics. This feedback loop ensures that the training process, while occurring in a virtual environment with reduced time and cost, still produces models that are highly adaptable and effective for real-world applications.
Data Source
AI summary
Training data generators and methods for machine learning are disclosed. An example method to generate training data for machine learning by generating simulated training data for a target neural network, transforming, with a training data transformer, the simulated training data form transformed training data, the training data transformer trained to increase a conformance of the transformed training data and the simulated training data, and training the target neural network with the transformed training data.