Synthetic Training Data Generation for Perception Models
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Solution Overview
Problem
Developing machine learning models for autonomous devices is a time-consuming and error-prone process due to the difficulty in gathering and processing training data, often requiring manual effort and lacking efficient methods to generate synthetic data that matches real-world scenarios.
Innovation Solution
A data science system that automates the generation of training data using simulated environments, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), to create synthetic training data that matches real-world properties, thereby improving the efficiency and quality of machine learning model development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data gathering and processing methods are used, then data quality can be ensured through human review, but the development time and cost increase significantly
Solution Approach 1:
The patent uses synthetic data generation to create copies of real-world training data through simulation environments. Instead of manually gathering and processing real data, the system generates synthetic representations that capture the essential characteristics and distributions of real-world scenarios, thereby reducing manual effort while maintaining data quality for model training
Solution Approach 2:
The system implements automated data generation and processing pipelines that operate without continuous human intervention. The simulation environments automatically generate training data, and the selection mechanisms autonomously identify suitable synthetic data samples, replacing manual data scientist efforts with self-service automated processes
2Quantity of substance
If synthetic data is generated to increase data availability, then training data quantity improves, but the complexity of generating realistic synthetic data increases
Solution Approach 1:
The patent introduces simulation environments as intermediary systems between real-world scenarios and training data generation. These simulations act as mediators that translate complex real-world physics and interactions into generateable synthetic data, simplifying the overall process while maintaining realism through the simulation layer
Solution Approach 2:
The system employs parameter-based control of simulation environments to generate diverse synthetic data. By adjusting simulation parameters (such as environmental conditions, object properties, and scenario variables), the system can efficiently generate varied training data without increasing fundamental system complexity, leveraging parameter variation to achieve data diversity
3Productivity
If automated data generation is implemented, then productivity increases, but the precision of matching real-world scenarios may decrease
Solution Approach 1:
The patent implements feedback mechanisms where the performance and characteristics of generated synthetic data are evaluated against real-world data distributions. This feedback loop allows the system to iteratively refine its data generation process, ensuring that automated generation maintains high fidelity to real-world scenarios while sustaining high productivity
Solution Approach 2:
The system employs dynamic adjustment of generation parameters and selection criteria based on the specific requirements of different training scenarios. Rather than using static generation rules, the system adapts its data generation process dynamically to match the desired real-world scenario characteristics, maintaining precision while automating the process
Data Source
AI summary
A method is provided. The method includes generating a set of candidate training data based on a training data generator. The method also includes training a first machine learning model based on the set of candidate training data. The first machine learning model generates a set of inferences during the training based on the set of candidate training data. The method further includes determining a set of importance factors based on the set of inferences and a second machine learning model. The method further includes updating the training data generator based on one or more distributions of properties determined based on the set of importance factors.