Machine Learning Domain Generalization With Dual-Network Training
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Solution Overview
Problem
Machine learning models typically perform poorly when there is a shift in data distribution between training and inference times, leading to significant performance degradation in real-world scenarios where data distributions differ.
Innovation Solution
A method involving two networks with different weight updating techniques is employed, using a compound error metric to train a machine learning model, where one network updates weights based on a gradient descent and the other uses a moving average technique, enabling the model to generalize across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is trained on data from a single domain, then the model achieves high accuracy on that specific domain, but the model performance degrades significantly when applied to data from different domains due to distribution shift
Solution Approach 1:
The patent divides the training process into two separate networks: an online network that processes images from the first domain and a target network that processes images from the second domain. Each network is trained separately on different domains, allowing the system to learn domain-specific features while maintaining the ability to generalize across domains through the compound error metric and weight updating mechanisms.
Solution Approach 2:
The patent creates a unified training framework that can handle multiple domains simultaneously. The compound error metric combines error metrics from both networks, and the weight updating mechanism uses information from both networks to update weights, enabling the system to function effectively across different domains without requiring separate models for each domain.
2Adaptability or versatility
If the model is trained to generalize across multiple domains, then domain adaptability improves, but classification accuracy on specific domains may decrease due to the complexity of learning multiple distributions
Solution Approach 1:
The patent implements a feedback mechanism where the compound error metric, derived from both the online network and target network results, is used to update the weights of both networks. This feedback loop allows the system to continuously improve its performance across domains by learning from its errors on both domains simultaneously, maintaining high accuracy while achieving domain generalization.
Solution Approach 2:
The patent changes the parameters (weights) of the networks dynamically during training based on the compound error metric. The weight updating mechanism adjusts the network parameters to minimize the combined error from both domains, allowing the model to adapt its parameters to perform well across multiple domains without sacrificing accuracy on individual domains.
3Ease of manufacture
If traditional weight updating methods are used for both networks, then the training process is simple, but the model cannot effectively capture domain differences and achieve domain generalization
Solution Approach 1:
The patent introduces dynamic weight updating mechanisms where the online network weights are updated based on the compound error metric and the target network weights are updated using a moving average technique dependent on the online network weights. This dynamic updating process allows the system to adapt to domain differences effectively while maintaining a relatively simple training framework that builds upon traditional methods.
Data Source
AI summary
Training a machine learning model for domain generalized operation includes processing, using computer hardware, a first plurality of images belonging to a first domain through a first network. A second plurality of images belonging to a second domain is processed using the computer hardware through a second network. A compound error metric is generated using the computer hardware from a plurality of plurality of error metrics derived from results generated from the processing of the first network and the processing of the second network. Weights of the first network are updated using the computer hardware based on the compound error metric. Weights of the second network are updated using the computer hardware using a moving average technique that is dependent on the weights of the first network as updated.