Synthetic Dataset Regeneration via Simulation Log Data
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Solution Overview
Problem
Existing systems cannot regenerate synthetic datasets using simulators, limiting modifications, enhancements, and error fixing capabilities.
Innovation Solution
The system generates log data during dataset creation, which includes parameters, values, assets, and results, allowing for recreation, modification, and enhancement of the dataset by inputting this log data back into the simulator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a simulator generates a dataset using random parameter sampling, then the dataset achieves high diversity and coverage, but the dataset cannot be regenerated or modified
Solution Approach 1:
The system performs preliminary action by logging all parameter sampling information, random seeds, and generation metadata during the initial dataset creation process. This advance recording enables subsequent regeneration and modification of the dataset without re-running the entire simulation process, directly resolving the contradiction between dataset diversity and regenerability.
Solution Approach 2:
Instead of regenerating the entire dataset from scratch, the system creates a reusable copy of the generation configuration and parameter sampling information in the form of structured log files. These logs serve as templates that can be copied and modified to generate new datasets with different parameters while maintaining the same generation process, enabling both diversity and regenerability.
2Reliability
If users want to modify or enhance a dataset, then dataset quality can be improved, but conventional systems lack the capability to regenerate the dataset
Solution Approach 1:
The system implements feedback by creating a closed-loop process where generation logs are stored and can be read back to regenerate datasets. Users can modify the logged parameter values and re-run the generation process, allowing iterative improvement of dataset quality while maintaining ease of operation through automated regeneration based on feedback from the logs.
Solution Approach 2:
The system enables parameter changes by storing all generation parameters in structured log files that can be easily modified. Users can change specific parameter values (such as scene configurations, object properties, or environmental conditions) in the logs and regenerate the dataset with these modified parameters, directly improving dataset quality while maintaining operational simplicity.
3Stability of the object's composition
If the same simulator is used to generate datasets, then consistency in generation process is maintained, but random sampling prevents exact regeneration
Solution Approach 1:
The system extracts the random sampling details from the generation process and stores them separately in structured log files. By taking out the random seeds, parameter values, and sampling configurations from the simulation execution and preserving them independently, the system maintains generation process consistency while enabling exact regeneration of datasets despite the random nature of sampling.
Data Source
AI summary
In various examples, synthetic dataset regeneration for AI systems and applications is described herein. For instance, systems and methods described herein may use a simulator to generate a synthetic dataset along with data (referred to, in some examples, as “log data”) representing information associated with the generation of the synthetic dataset by the simulator. For instance, the log data may represent at least parameters used to generate synthetic dataset, values for the parameters, assets associated with the parameters, and/or values representing results associated with the synthetic dataset. The systems and methods may then use the log data to recreate, modify, and/or enhance the synthetic dataset. For example, the synthetic dataset may be recreated by providing at least the log data as input to the simulator such that the simulator regenerates the dataset using the same parameters, values, and/or assets.