Neural Network Domain Adaptation via Attention-Weighted Synthesis
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Solution Overview
Problem
Machine learning systems face domain shift issues when statistical distributions of training and inference data differ, leading to reduced predictive accuracy, especially during product generation changes, where obtaining new annotations is costly and time-consuming.
Innovation Solution
A method for unsupervised domain adaptation using attention maps to weight differences between source and target domain images, allowing for automated identification of important regions and enabling accurate transfer of objects from source to target domain without requiring extensive annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new training data with annotations is collected for each product generation, then predictive accuracy is maintained, but time and cost increase significantly
Solution Approach 1:
The patent uses a generator network to synthesize target domain images from source domain images, creating artificial training data that mimics the target domain distribution. This copying approach eliminates the need to collect and annotate real target domain data, significantly reducing time and cost while maintaining training effectiveness
Solution Approach 2:
The patent introduces a generator network as an intermediary between source and target domains. This intermediary transforms source domain images into target domain-like images, enabling domain adaptation without direct access to annotated target domain data, thus resolving the contradiction between accuracy and annotation cost
2Measurement precision
If conventional domain adaptation methods are used without attention maps, then the process is simpler, but adaptation accuracy decreases
Solution Approach 1:
The patent applies attention maps to weight different regions of images differently during domain adaptation. By focusing transformation on important local regions (identified by attention mechanisms) rather than treating all pixels equally, the method achieves higher adaptation accuracy while adding manageable complexity through region-specific processing
3Reliability
If extensive annotations are obtained for target domain data, then training accuracy improves, but cost increases
Solution Approach 1:
The patent generates synthetic target domain images from source domain images using a trained generator network. These synthesized images serve as training data without requiring costly manual annotations, thereby maintaining training accuracy while eliminating annotation costs for the target domain
Solution Approach 2:
The system performs self-service by automatically generating its own training data through the generator network. The synthesized images are used to train the translation model, creating a self-sufficient training loop that does not depend on external annotated data, thus reducing both time and cost
Data Source
AI summary
Computer-implemented method for training a machine learning system. The method includes: providing a source image from a source domain and a target image of a target domain; determining a first generated image based on the source image using a first generator, and determining a first reconstruction based on the first generated image using a second generator; determining a second generated image based on the target image using the second generator, and determining a second reconstruction based on the second generated image using the first generator; determining a first loss value, the first loss value characterizing a first difference between the source image and the first reconstruction, and determining a second loss value, the second loss value characterizing a second difference between the target image and the second reconstruction; and training the machine learning system based on the first loss value and/or the second loss value.