Shift-Agnostic Weight Regularization for Neural Network Adaptation
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Solution Overview
Problem
Deep neural networks face performance degradation when deployed from a source domain to a target domain due to distribution gaps, especially when all model parameters are updated using unsupervised objective functions without ground truth data.
Innovation Solution
Implement shift-agnostic weight regularization, where shift-agnostic weights are penalized more than shift-biased weights during test-time adaptation, and use unlabeled online data to adapt the model to the target domain without performance degradation, leveraging a penalty vector and nearest source prototypes to align source and target features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all model parameters are updated using unsupervised objective functions on target domain data, then the model adapts to the target domain, but performance degradation occurs due to distribution gap
Solution Approach 1:
The patent applies different regularization strengths to different weight parameters based on their sensitivity to distribution shift. Shift-agnostic weights (those sensitive to distribution gap) receive higher regularization penalties, while shift-biased weights (those robust to distribution gap) receive lower penalties. This localized differentiation resolves the contradiction by protecting performance-critical weights from harmful updates while allowing adaptation in robust weight regions.
Solution Approach 2:
Instead of uniformly encouraging all weight updates to adapt to the target domain, the patent inverts the approach by penalizing updates to shift-agnostic weights. This inverted strategy prevents harmful updates to performance-critical weights while still allowing adaptation in other regions, thereby resolving the performance degradation issue during target domain adaptation.
2Reliability
If the model is trained on source domain data, then the model achieves good performance on source domain, but performance degrades when deployed on target domain with large distribution gap
Solution Approach 1:
The patent performs preliminary identification of shift-agnostic and shift-biased weights during source domain training. By pre-characterizing which weights are sensitive to distribution shift before deployment, the model is prepared to protect these critical weights during subsequent target domain adaptation, thereby maintaining source domain performance while enabling target domain adaptability.
Solution Approach 2:
The patent differentiates weight parameters into two categories (shift-agnostic and shift-biased) with different properties regarding distribution shift sensitivity. This local quality differentiation allows the model to maintain source domain performance by protecting shift-agnostic weights while adapting to target domain through updates to shift-biased weights.
3Ease of manufacture
If uniform regularization is applied to all weights during test-time adaptation, then the implementation is simple, but performance degradation occurs due to inappropriate penalty on shift-agnostic weights
Solution Approach 1:
The patent moves from uniform regularization to localized regularization where different penalty strengths are applied to different weight groups. By identifying and categorizing weights into shift-agnostic and shift-biased groups, the patent enables differentiated regularization that protects performance while maintaining computational efficiency through group-level rather than parameter-level customization.
Data Source
AI summary
A processor-implemented method includes training a machine learning model on a source domain. The method also includes testing the machine learning model on a target domain, after training. The method further includes training the machine learning model on the target domain by regularizing weights of the machine learning model such that shift-agnostic weights are subjected to a higher penalty than shift-biased weights.