Inference-Invariant Batch Normalization for Neural Network Adaptation
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Solution Overview
Problem
The existing methods for adapting neural networks with batch normalization layers face inefficiencies due to internal covariate shift, leading to performance degradation in domain adaptation tasks, particularly in acoustic modeling, where the mismatch between training and inference modes affects the feature distributions and requires retraining with limited adaptation data.
Innovation Solution
The proposed method, Inference-Invariant Batch Normalization (IIBN), adapts only the batch normalization layers using adaptation data, recalculating statistics and adjusting scale and shift parameters to maintain consistency between training and inference modes, allowing the neural network to leverage the forward pass from the original domain as a starting point for domain adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the whole neural network is retrained using adaptation data, then the domain adaptation performance is improved, but the training time and computational cost increase significantly
Solution Approach 1:
The patent segments the neural network into two parts: batch normalization layers and other layers. Only the batch normalization layers are adapted using adaptation data, while the other layers remain frozen. This segmentation allows selective adaptation that improves domain adaptation performance without requiring full network retraining, thus reducing training time and computational cost.
Solution Approach 2:
The patent extracts and isolates the batch normalization layers from the rest of the neural network for separate adaptation. By taking out only the batch normalization parameters (mean and variance) for adaptation while keeping the rest of the network parameters fixed, the method achieves effective domain adaptation with minimal training time and computational resources.
2Productivity
If only batch normalization layers are adapted, then the training efficiency is improved, but the adaptation performance may degrade due to distribution mismatch
Solution Approach 1:
The patent changes the parameters of batch normalization layers (mean and variance) to match the distribution statistics of the target domain. By computing new mean and variance from adaptation data and updating the batch normalization layers accordingly, the method maintains distribution consistency between training and inference modes, ensuring adaptation performance while preserving training efficiency.
3Adaptability or versatility
If the neural network is adapted to target domain distribution, then the domain adaptation capability is improved, but the original domain performance may deteriorate
Solution Approach 1:
The patent implements a dynamic adaptation approach where batch normalization parameters are adjusted based on the target domain distribution while keeping the rest of the network parameters fixed. This dynamic adjustment of only the batch normalization layers allows the network to adapt to the target domain without altering the learned features from the source domain, thus maintaining original domain performance while improving adaptability.
Data Source
AI summary
A method, computer program product, and apparatus for adapting a trained neural network having one or more batch normalization layers are provided. The method includes adapting only the one or more batch normalization layers using adaptation data. The method also includes adapting the whole of the neural network having the one or more adapted batch normalization layers, using the adaptation data.