Bi-Regressor Neural Network Training for Cross-Environment Localization
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Solution Overview
Problem
Existing domain adaptation approaches for regression tasks face challenges in adapting a regressor to a new environment due to the lack of a referee to judge the quality of extracted features, and the difference in loss functions between classification and regression tasks, leading to performance degradation when processing unlabeled data.
Innovation Solution
A bi-regressor neural network with a three-stage training procedure is employed, using a feature extractor and two regressors with identical architectures, where the regressors serve as referees to identify similar and different distributions, and an adversarial discriminator is used to extract domain-invariant features, mitigating domain shift through data augmentation and adversarial training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard regressor is trained with labeled data from a source domain, then it achieves good localization accuracy in the source domain, but it suffers from domain shift and performs poorly when applied to unlabeled data from a target domain
Solution Approach 1:
The patent introduces an adversarial discriminator as an intermediary component that mediates between the feature extractor and the regressor. The discriminator acts as a referee to evaluate whether extracted features come from the source or target domain, forcing the feature extractor to learn domain-invariant representations that work across both domains, thus resolving the domain shift problem while maintaining localization accuracy
Solution Approach 2:
The patent segments the traditional single regressor architecture into a bi-regressor system with two separate regressors (one for source domain, one for target domain) sharing a common feature extractor. This segmentation allows each regressor to specialize in its respective domain while the shared feature extractor learns universal features, improving both source domain accuracy and target domain adaptability
2Adaptability or versatility
If domain adaptation techniques are applied to transfer knowledge from source to target domain, then adaptability to new environments improves, but the performance on labeled source domain data may deteriorate due to domain shift
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting the domain adaptation strength through a domain adaptation coefficient that balances source domain and target domain losses during training. This allows the model to adapt parameters optimally for each domain while maintaining overall performance consistency across domains
Solution Approach 2:
The adversarial discriminator provides feedback to the feature extractor by classifying whether features come from source or target domain. This feedback mechanism guides the feature extractor to generate domain-invariant features, ensuring that the model maintains reliable performance across both domains while adapting to new environments
3Measurement precision
If a referee mechanism is introduced to judge feature quality in domain adaptation, then feature extraction quality improves, but the system complexity increases due to additional components
Solution Approach 1:
The adversarial discriminator serves multiple functions: it acts as a referee to judge feature domain origin, provides gradient feedback for domain-invariant feature learning, and enables the bi-regressor system to adapt to both source and target domains. This multi-functionality justifies the added complexity by delivering comprehensive improvements in feature extraction quality and model adaptability
Data Source
AI summary
The present disclosure provides a method and a system for training a neural network suitable for localization of a device within an environment based on signals received by the device. The method comprises training a bi-regressor neural network to identify locations from labeled data, wherein the bi-regressor neural network includes a feature extractor and a bi-regressor including two regressors; training parameters of the bi-regressor using the labeled data and unlabeled data, such that each of the two regressors identifies the same labeled locations while processing the labeled data and identifies different locations while processing the unlabeled data; and training parameters of the feature extractor using an adversarial discriminator to extract domain invariant features from the unlabeled data with statistical properties of the labeled data according to the adversarial discriminator such that each of the two regressors identifies the same locations while processing the domain invariant features.