Neural Network Pre-Training to Reject Out-of-Domain Artifacts
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Solution Overview
Problem
Current 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 neural networks using synthetic data to restrict the domain, where lower-level feature layers are updated during synthetic data training and higher-level policy layers are fixed, allowing the network to adapt to real-world data while rejecting out-of-domain artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world image data is used to train the neural network, then the network can learn in-domain features relevant to the domain of interest, but it also learns out-of-domain artifacts that are less relevant and may lead to incorrect predictions
Solution Approach 1:
The training process is segmented into two distinct phases: pre-training on synthetic data followed by fine-tuning on real-world data. This segmentation allows the network to first learn clean in-domain features without artifacts, then adapt to real-world variations while maintaining focus on relevant features through controlled updating of only lower-level layers.
Solution Approach 2:
The neural network undergoes preliminary training on synthetic data before being exposed to real-world data. This preliminary action establishes a clean feature foundation that filters out out-of-domain artifacts, allowing the subsequent real-world training to build upon this purified feature set rather than learning artifacts from the start.
2Measurement precision
If a large set of real-world training data is generated to capture high variability, then the neural network can accurately interpret real-world image data, but the data generation process becomes resource intensive and time consuming
Solution Approach 1:
Instead of collecting vast amounts of real-world training data, the invention creates synthetic copies of the target domain through simulated 3D environments. These synthetic images replicate the essential visual characteristics and variability of real-world scenes, providing sufficient training material without the resource-intensive data collection process.
Solution Approach 2:
The synthetic data generation process systematically varies parameters such as lighting conditions, camera angles, object positions, and environmental factors to capture the high variability present in real-world scenarios. This parameter-based approach efficiently generates diverse training data without requiring physical data collection for each variation.
3Productivity
If the neural network is trained only on synthetic image training data, then the training process is less resource intensive and out-of-domain artifacts are controlled, but the network does not perform well in real-world deployment due to inability to capture high variability and detail
Solution Approach 1:
The training approach is segmented into two stages: initial training on synthetic data for efficiency and artifact control, followed by fine-tuning on real-world data for reliability. This segmentation allows each stage to optimize for its specific goal while contributing to overall system performance.
Solution Approach 2:
Synthetic data training serves as a preliminary action that prepares the network with efficient, artifact-free feature learning before the network encounters real-world data. This preliminary training establishes a solid foundation that improves real-world performance while maintaining training efficiency.
4Measurement precision
If synthetic training data is combined with real-world training data, then the training data set size is increased and neural network performance improves, but the network may still learn undesirable correlations between domain features and out-of-domain artifacts
Solution Approach 1:
The network undergoes preliminary training on clean synthetic data before being exposed to real-world data that may contain artifacts. This preliminary action establishes correct feature associations early in training, preventing the formation of undesirable correlations between domain features and artifacts that would occur if real-world data were used from the start.
Solution Approach 2:
The training process is segmented so that synthetic data training occurs first to establish clean feature learning, followed by real-world data training that builds upon this foundation. This sequential segmentation prevents contamination of feature learning with artifacts while still benefiting from real-world variability.
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.