Batch Normalization Statistics for Unseen Domain Generalization
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Solution Overview
Problem
Machine learning models trained on a specific data distribution often fail 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
A method that utilizes batch normalization statistics to map different domains into a shared latent space, allowing for domain-specific predictions to be interpolated based on similarity scores between batch normalization statistics, creating a lightweight ensemble model that generalizes to unseen domains without requiring additional training data.
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 the requirement for annotated samples from test domain increases
Solution Approach 1:
The patent introduces batch normalization statistics as an intermediary that captures domain-specific characteristics without requiring annotated target domain samples. These statistics serve as a mediator between source domain training data and target domain generalization, enabling the model to adapt to unseen domains through statistical properties rather than explicit target domain annotations
Solution Approach 2:
The patent changes the parameters used for domain adaptation from traditional feature extraction parameters to batch normalization statistics parameters. By utilizing the running mean and variance stored in batch normalization layers, the method transforms how domain characteristics are represented, allowing generalization to unseen domains without requiring target domain annotated samples
2Adaptability or versatility
If explicit domain-specific information is discarded to achieve domain invariance, then model robustness to unseen domains is improved, but domain-specific training data utilization is reduced
Solution Approach 1:
The patent segments the model into shared parameters and domain-specific batch normalization statistics. This segmentation allows the model to maintain domain-invariant feature extraction through shared parameters while preserving domain-specific information through separate batch normalization statistics for each source domain, which are then used to guide generalization to target domains
Solution Approach 2:
The patent adds a new dimension for representing domain characteristics by utilizing the batch normalization statistics (mean and variance) as a separate parameter space. This additional dimension captures domain-specific information without interfering with the domain-invariant feature learning, enabling the model to leverage both domain-specific and domain-general information simultaneously
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 lightweight ensemble model that operates within such latent space.