Neural Network Pre-Training to Restrict Out-of-Domain Features
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Solution Overview
Problem
Existing approaches to training neural networks for image processing, such as in autonomous vehicles, face challenges in accurately distinguishing between in-domain and out-of-domain features, leading to resource-intensive data generation and potential failures in real-world deployments due to learning undesirable correlations between domain features and artifacts.
Innovation Solution
The method involves pre-training a neural network using synthetic data to focus on in-domain features and then updating only the lower-level feature layers during a subsequent training phase with real-world data, while keeping higher-level policy layers fixed to reject out-of-domain artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world image training data is used to train the neural network, then the network can learn from actual environmental conditions, but the cost and time required for data generation increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on synthetic data before deploying it to real-world environments. This preparatory training phase allows the network to learn basic features and patterns from controlled synthetic images, reducing the amount of real-world data needed and the time required for subsequent training and deployment.
2Adaptability or versatility
If real-world image training data is used, then the network learns from diverse environmental conditions, but the network may learn undesirable correlations between domain features and out-of-domain artifacts
Solution Approach 1:
The patent applies segmentation by separating the training process into distinct phases: first training on synthetic data to learn domain-specific features, then fine-tuning with real-world data. This segmentation allows the network to first establish correct feature associations without artifacts, then adapt to environmental variability without learning spurious correlations.
Solution Approach 2:
The patent uses synthetic data as an intermediary between controlled training environments and complex real-world deployments. This intermediary training dataset allows the network to learn fundamental patterns without exposure to real-world artifacts, serving as a bridge that prevents direct learning of undesirable correlations while still preparing the network for real-world variability.
3Loss of time
If synthetic image training data is used, then the cost and time of data generation is reduced, but the network may not perform well in real-world deployment due to lack of high variability and detail
Solution Approach 1:
The patent applies preliminary action by using synthetic data for initial training to establish a strong foundation of domain-specific knowledge, then performing subsequent fine-tuning with real-world data to adapt to actual environmental variability and details, ensuring both efficiency and real-world performance.
Solution Approach 2:
The patent applies dynamics by making the training process adaptive and multi-stage, transitioning from static synthetic data training to dynamic real-world data fine-tuning. This allows the network to first learn from controlled synthetic environments efficiently, then dynamically adapt to the complexity and variability of real-world conditions.
Data Source
AI summary
A neural network is trained to focus on a domain of interest. For example, in a pre-training phase, the neural network in trained using synthetic training data, which is configured to omit or limit content less relevant to the domain of interest, by updating parameters of the neural network to improve the accuracy of predictions. In a subsequent training phase, the pre-trained neural network is trained using real-world training data by updating only a first subset of the parameters associated with feature extraction, while a second subset of the parameters more associated with policies remains fixed.