Unsupervised Domain Adaptation via Augmented Bridge Domains
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Solution Overview
Problem
Conventional unsupervised domain adaptation methods face significant domain mismatch issues when directly adapting from a source domain to a target domain, requiring labor-intensive labeling and inefficient transfer of learning results.
Innovation Solution
The method generates augmented domains for both source and target domains using a mixup technique with fixed ratios, enabling bidirectional matching to bridge the domains and update weights based on consistency regularization, thereby reducing domain mismatch and improving learning transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If direct adaptation from source domain to target domain is used, then the adaptation process is simple, but large domain mismatch occurs
Solution Approach 1:
The patent introduces an augmented domain as an intermediary between the source domain and target domain. This augmented domain is generated by applying mixup technique to source domain data, creating a bridge that gradually adapts the model from source to target domain, thereby reducing the direct domain mismatch while maintaining a manageable adaptation process.
2Reliability
If augmented domains with fixed ratio mixup are generated, then domain mismatch is reduced, but the generation process becomes more complex
Solution Approach 1:
The patent applies mixup technique with a fixed ratio parameter (α) to transform source domain data into augmented domain data. By controlling the mixup ratio parameter, the method systematically adjusts the interpolation between source and target domain samples, reducing domain mismatch through parameterized data transformation while keeping the process manageable.
3Productivity
If bidirectional matching between augmented domains is performed, then learning transfer efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent implements bidirectional matching where the model learns to match features between augmented domain and target domain in both directions. This dynamic two-way adaptation process allows the model to progressively align feature distributions, improving learning transfer efficiency by enabling mutual refinement of domain alignment rather than unidirectional adaptation.
Data Source
AI summary
The present invention relates to a method and apparatus for unsupervised domain adaptation, including the steps of generating a first augmented domain associated with a source domain, generating a second augmented domain associated with a target domain, and performing unsupervised domain adaptation by using the first augmented domain and the second augmented domain as a bridge for connecting the source domain and the target domain, and it is possible to apply to other exemplary embodiments.