Cross-Domain Batch Normalization for Neural Network Adaptation
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Solution Overview
Problem
Machine learning systems face challenges in recognizing actions in one domain, such as computer games, using a training set from another domain, like motion picture video, due to domain mismatch issues.
Innovation Solution
A generic domain adaptation module is established using a loss function and/or a neural network, which receives input from multiple output points of two training domains in deep learning, providing an output measure for optimization. This module includes a cross-domain feature normalization module that can be inserted into any layer of the neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network is trained on real world video data, then it achieves good performance on real world data, but it suffers from dataset shift and poor performance when applied to game video data
Solution Approach 1:
The patent introduces a domain adaptation module as an intermediary component between the feature extractor and classifier. This module includes a domain classifier and gradient reversal layer that acts as a mediator to learn domain-invariant features. The domain adapter receives features from the source domain and transforms them to be suitable for the target domain, thereby resolving the contradiction between source domain performance and target domain adaptability.
Solution Approach 2:
The patent changes the parameters of the neural network during domain adaptation by introducing learnable transformation parameters in the domain adaptation module. The domain classifier and gradient reversal layer modify the feature representations through learned transformations, changing the parameter space to achieve domain invariance. This allows the model to maintain source domain performance while adapting to target domain characteristics.
2Measurement precision
If multiple domain-specific neural networks are trained separately, then each network achieves high accuracy on its specific domain, but the system complexity increases
Solution Approach 1:
The patent merges the source domain network and target domain network into a unified architecture with shared components. The feature extractor is shared between domains, and the domain adaptation module combines features from both domains. This merging approach maintains high classification accuracy by leveraging domain-specific knowledge while reducing system complexity through parameter sharing and a unified processing pipeline.
Solution Approach 2:
The domain adaptation module serves multiple functions: it acts as a feature transformer, a domain classifier, and a gradient reversal mechanism all within a single component. This multi-functionality reduces the need for separate specialized modules for each domain, thereby maintaining measurement precision while reducing device complexity through a more universal and streamlined architecture.
3Stability of the object's composition
If traditional batch normalization is applied separately to each domain, then domain-specific statistics are preserved, but cross-domain feature alignment is hindered
Solution Approach 1:
The patent introduces a cross-domain batch normalization layer as an intermediary between domain-specific batch normalization layers. This intermediate layer receives normalized features from source and target domains, computes unified statistics across domains, and outputs aligned features. This mediator preserves domain-specific compositions through initial domain-specific normalization while achieving cross-domain alignment through the unified normalization step.
Solution Approach 2:
The normalization system uses a composite approach combining domain-specific batch normalization layers with a cross-domain batch normalization layer. Each component serves a specific function: domain-specific layers preserve local feature distributions while the cross-domain layer aligns global feature spaces. This composite normalization strategy maintains stability of domain compositions while achieving precision in cross-domain feature alignment.
Data Source
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AI summary
A domain adaptation module (1800) is used to optimize a first domain (1804) derived from a second domain (1802) using respective outputs from respective parallel hidden layers of the domains.