Batch Normalization Statistics for Unseen-Domain Generalization
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Solution Overview
Problem
Machine learning models trained on a specific data distribution struggle to generalize to samples from different distributions, a phenomenon known as domain shift, which is a significant limitation in data-driven algorithms, especially in real-world applications where collecting samples from all possible environments is unfeasible.
Innovation Solution
Utilizing batch normalization statistics to map input samples to a latent space where domain membership is measured by distance from domain centroids, enabling a lightweight ensemble model that shares most parameters except for normalization statistics, allowing interpolation of domain-specific predictions based on similarity scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain adaptation techniques are used to mitigate domain shift, then model performance on target domain is improved, but additional annotated samples from test domain are required which are often unfeasible to collect
Solution Approach 1:
The patent creates virtual copies of source domain data by applying style transfer and domain adaptation techniques to generate synthetic target domain samples. These copied representations allow the model to learn domain-invariant features without requiring actual annotated samples from the target domain, thus resolving the contradiction between improving target domain performance and avoiding the need for additional annotated samples.
Solution Approach 2:
The patent performs preliminary domain adaptation during the training phase by pre-processing source domain data to match target domain distributions. This preliminary action of adapting data distributions before model training eliminates the need for subsequent fine-tuning with target domain samples, allowing the model to be directly deployed on unseen domains without requiring additional annotated samples.
2Adaptability or versatility
If domain generalization techniques discard domain-specific information to achieve domain invariance, then model robustness to unseen domains is improved, but domain-specific training data is not fully utilized
Solution Approach 1:
The patent segments the model into domain-specific and domain-invariant components. By separating these functions, the model can maintain domain-specific knowledge in dedicated modules while using domain-invariant modules for generalization. This segmentation allows the model to utilize domain-specific training data effectively without sacrificing robustness to unseen domains, resolving the contradiction between adaptability and information loss.
3Adaptability or versatility
If multiple training sets are used to learn domain-invariant feature extractor, then generalization capability is improved, but model complexity and training requirements increase
Solution Approach 1:
The patent designs a universal domain adaptation layer that can handle multiple domains and distribution shifts through a single unified mechanism. This multi-functional component replaces the need for separate domain-specific adapters or multiple specialized models, achieving high generalization capability while maintaining relatively simple model architecture and reducing training complexity.
Data Source
AI summary
Generally, the present disclosure is directed to systems and methods that leverage batch normalization statistics as a way to generalize across domains in particular, example implementations of the present disclosure can generate different representations for different domains by collecting independent batch normalization statistics, which can then be used to map between domains in a shared latent space. At test or inference time, samples from an unknown test or target domain can be projected into the same shared latent space. The domain of the target sample can therefore be expressed as a linear combination of the known ones, with the combination between weighted based on respective distances between batch normalization statistics in the latent space. This same mapping strategy can be applied at both training and test time to learn both a latent representation and a powerful but light-weight ensemble model that operates within such latent space.