Target-Domain Adaptation Through Machine Learning Model Pruning
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Solution Overview
Problem
Domain shift in computer vision models due to variations in image properties and environmental factors leads to decreased accuracy and reliability in real-world scenarios, as the models fail to generalize well to unseen data from the target domain.
Innovation Solution
A method involving dataset and model pruning is employed to adapt a machine learning model for a specific target domain, using a labeled training dataset pruned based on an unlabeled dataset from the target domain, without requiring fine-tuning, to align the model with the target domain distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is trained on source domain data, then the model can be developed with initial capabilities, but the model performance degrades when deployed to target domain due to domain shift
Solution Approach 1:
The patent extracts and removes source domain specific features from the trained model by identifying and eliminating features that are correlated with source domain characteristics but not target domain. This is achieved through feature importance analysis and selective removal, allowing the model to retain only domain-invariant features that generalize well to the target domain.
Solution Approach 2:
The patent changes the parameters of the trained model by adjusting feature weights and selecting different feature subsets based on target domain performance. This involves modifying the model's internal parameters through feature selection algorithms that prioritize features demonstrating consistency across domains, thereby adapting the model to the target domain without complete retraining.
2Reliability
If domain adaptation techniques are applied to improve target domain performance, then model accuracy improves, but the complexity of the process increases
Solution Approach 1:
The patent performs preliminary feature selection and model preparation in advance by pre-processing source domain data to identify potential domain-invariant features before target domain deployment. This preliminary action includes creating feature representations that are designed to be domain-agnostic, reducing the complexity of adaptation when the model is deployed to the target domain.
3Measurement precision
If the model is trained with comprehensive labeled data from source domain, then the initial model capabilities are enhanced, but the model fails to generalize to target domain due to data distribution mismatch
Solution Approach 1:
The patent segments the feature space into source domain specific features and domain-invariant features. By dividing the feature set and selectively retaining only the domain-invariant segments, the model maintains its source domain performance while gaining the ability to generalize to the target domain. This segmentation is achieved through statistical analysis of feature distributions across domains.
Data Source
AI summary
In one implementation, a device receives, via a user interface, a selection of a labeled training dataset and a selection of an unlabeled training dataset, wherein the unlabeled training dataset is captured from a target domain. The device forms a domain-adapted training dataset by pruning the labeled training dataset based on the unlabeled training dataset. The device trains a machine learning model using the domain-adapted training dataset. The device prunes the machine learning model to form a domain-adapted model for the target domain.