Synthetic Data Generation for Imitation Learning Failure Cases
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Solution Overview
Problem
The challenge lies in providing accurate training for machine learning models, particularly deep neural networks, as they often require large amounts of diverse real data, which is expensive and time-consuming to obtain, and using synthetic data can lead to overfitting and inaccurate results due to insufficient data selection methods.
Innovation Solution
The approach involves a combination of real and synthetic training data, where synthetic data is generated specifically to address failure cases in the training process, using a circular process of evaluation, simulation, and retraining to improve data distribution and balance, thereby enhancing the performance of machine learning models in applications like object detection and autonomous driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If synthetic data is used to train machine learning models, then training cost and time are reduced, but the model accuracy deteriorates due to overfitting on synthetic data
Solution Approach 1:
The patent applies local quality by generating synthetic data with specific characteristics that match particular failure cases or edge scenarios in the training data. Instead of using generic synthetic data, the system creates targeted synthetic samples with localized features (specific object types, environmental conditions, or error patterns) to address specific weaknesses in the model without causing overall overfitting.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying parameters of synthetic data generation (such as object positions, lighting conditions, weather patterns, or camera angles) to create diverse training samples. This allows the model to learn robust features across different parameter configurations while maintaining accuracy on real data through controlled parameter variation rather than excessive synthetic data volume.
2Measurement precision
If real data is collected and annotated to improve training quality, then model accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent applies copying by creating synthetic replicas of real data scenarios through simulation. Instead of collecting and annotating additional real data, the system copies existing real data patterns and generates synthetic versions with varied characteristics, preserving the essential features needed for training while eliminating the time-consuming data collection and annotation processes.
Solution Approach 2:
The patent uses preliminary action by pre-generating synthetic training data for anticipated failure cases and edge scenarios before actual model training begins. This proactive approach prepares comprehensive training coverage in advance, eliminating the need for iterative real data collection during model development and reducing overall training time.
3Ease of manufacture
If conventional synthetic data generation is used, then data production cost is reduced, but data distribution balance deteriorates leading to insufficient coverage of failure cases
Solution Approach 1:
The patent implements feedback by using model performance evaluation results to guide synthetic data generation. The system identifies failure cases and under-represented scenarios through model testing, then uses this feedback information to generate targeted synthetic data that specifically addresses the identified weaknesses, improving data distribution balance through iterative refinement.
Solution Approach 2:
The patent applies dynamics by making the synthetic data generation process adaptive and dynamic rather than static. The system continuously adjusts generation parameters and targets based on model performance feedback, allowing the data distribution to evolve and balance itself dynamically as the model improves, ensuring comprehensive coverage of failure cases.
Data Source
AI summary
Approaches presented herein provide for the generation of synthetic data to fortify a dataset for use in training a network via imitation learning. In at least one embodiment, a system is evaluated to identify failure cases, such as may correspond to false positives and false negative detections. Additional synthetic data imitating these failure cases can then be generated and utilized to provide a more abundant dataset. A network or model can then be trained, or retrained, with the original training data and the additional synthetic data. In one or more embodiments, these steps may be repeated until the evaluation metric converges, with additional synthetic training data being generated corresponding to the failure cases at each training pass.