Neural Network Training With Semantic Patch Matching for Domain Shift
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Solution Overview
Problem
Machine learning systems face domain shift issues when the statistical distribution of data at inference time differs from the training data, leading to reduced prediction accuracy, particularly in systems like semi-autonomous vehicles and camera sensors, where adapting to new product generations is challenging due to the cost and difficulty of obtaining annotated training data.
Innovation Solution
A method for training a machine learning system by assigning pixel-by-pixel meta-information to source and target patches, using similarity measures and attention maps to select patches with similar semantic content, and employing generators to adapt the source domain to the target domain, enabling precise object transfer and reducing the need for target domain annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random patches are selected from source and target domains for training, then the training process is simple, but the machine learning method lacks sufficient accuracy due to semantic content differences
Solution Approach 1:
The patent applies local quality by making the patch selection process adaptive based on semantic similarity. Instead of uniform random selection, the method evaluates semantic content using meta-information and similarity measures to selectively choose patches that are semantically compatible between source and target domains. This localized adaptation of the selection criterion resolves the contradiction by maintaining simplicity while improving accuracy through intelligent patch pairing.
2Measurement precision
If annotated training data is obtained from target domain through manual annotation, then the machine learning system achieves high accuracy, but the process becomes costly and time-consuming
Solution Approach 1:
The patent applies the copying principle by using meta-information (such as semantic segmentations, depth maps, or feature representations) as proxies for full manual annotations. Instead of requiring time-consuming pixel-level manual labeling of target domain images, the method copies or generates meta-information through automated processes like pre-trained networks or synthetic data generation, thereby maintaining high accuracy while dramatically reducing annotation time and cost.
3Adaptability or versatility
If complete images are transferred from source domain to target domain, then the domain adaptation is comprehensive, but the model complexity becomes unmanageable
Solution Approach 1:
The patent applies segmentation by dividing complete images into smaller patches and processing them individually or in groups. This segmentation approach enables comprehensive domain adaptation by allowing selective transformation of different image regions based on their semantic content, while keeping the model complexity manageable through localized processing. The patch-based approach reduces computational burden compared to processing entire images while maintaining adaptability through semantic-aware selection.
Data Source
AI summary
A computer-implemented method for training a machine learning system for transferring images of a source domain into a target domain. The method includes: ascertaining source patches based on source images of a source domain and target patches based on target images of a target domain, the source patches and the target patches each being assigned pixel-by-pixel pieces of meta-information; ascertaining tuples, each including one source patch and at least one target patch which characterizes a neighbor of the source patch nearest to k according to a similarity measure, k being a hyperparameter of the method and the similarity measure characterizing a similarity between a source patch and a target patch based on the pixel-by-pixel meta-information of the source patch and of the target patch; training the machine learning system based on the source patches of the tuples and on the target patches of the tuples.