Graph-Based Domain Generalization Framework
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Solution Overview
Problem
Machine learning models face poor generalizability due to distributional shifts between training and testing data, as conventional domain generalization methods fail to effectively utilize prior knowledge about the target domain.
Innovation Solution
A graph-based adaptive domain generation framework is employed, involving domain prototypical network training, construction of an autoencoding domain relation graph using a graph autoencoder, and domain-adaptive classifier training to produce informed decisions, leveraging prior knowledge and domain node embeddings for prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional domain generalization methods are used that only leverage source domain data, then the model can be trained without target domain data, but the model generalizability remains poor due to distributional shift
Solution Approach 1:
The patent applies preliminary action by encoding prior knowledge about the target domain into the training process before actual deployment. Specifically, the method incorporates prior knowledge representations during the training phase on source domains, allowing the model to be pre-prepared for target domain characteristics even though no target domain data is available for training. This resolves the contradiction by enabling the model to leverage prior knowledge in advance, improving generalizability without requiring target domain data during training.
2Measurement precision
If the model is trained to achieve good performance on source domains, then the model fits the training data well, but the model fails to generalize to unseen deployment environments due to distributional shift
Solution Approach 1:
The patent applies parameter changes by transforming the representation of domain knowledge from raw data parameters to structured prior knowledge parameters. The method changes the parameter space by encoding domain characteristics (such as optical network configurations including transceiver types, modulation types, and light power levels) into a standardized prior knowledge representation that can be systematically integrated into the model training, thereby improving adaptability while maintaining training effectiveness.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a prior knowledge representation layer that mediates between source domain training data and target domain deployment. This intermediary structure allows the model to learn from source domains while being guided by prior knowledge about target domain characteristics, effectively bridging the distributional gap and improving generalization without sacrificing training accuracy.
3Reliability
If target domain data is used for training, then the model can achieve best performance on the target domain, but the model requires target domain data availability during the development phase which is often unavailable
Solution Approach 1:
The patent applies the extraction principle by separating the target domain information into two components: (1) prior knowledge that can be obtained through domain expertise and documentation, and (2) actual target domain data that would ideally be used for training. The method extracts and utilizes only the prior knowledge component during training, while deliberately excluding the requirement for actual target domain data. This allows the model to achieve good target domain performance without needing target domain data availability during the development phase.
Data Source
AI summary
A computer-implemented method for employing a graph-based adaptive domain generation framework is provided. The method includes, in a training phase, performing domain prototypical network training on source domains, constructing an autoencoding domain relation graph by applying a graph autoencoder to produce domain node embeddings, and performing, via a domain-adaptive classifier, domain-adaptive classifier training to make an informed decision. The method further includes, in a testing phase, given testing samples from a new source domain, computing a prototype by using a pretrained domain prototypical network, inferring node embedding, and making a prediction by the domain-adaptive classifier based on the domain node embeddings.