Image Segmentation via Duplicate Layer Feature Matching
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Solution Overview
Problem
Image segmentation methods face inaccuracies when the domain distribution between training and actual data differs, and require high costs for measured data and label information, which can be difficult to obtain.
Innovation Solution
The method involves training duplicate layers of a pre-trained model to match features between training and target images, using a discriminator neural network to adjust the duplicate layers for similarity, and combining these layers to generate a new model for semantic segmentation without needing actual measured data or label information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-tuning is performed using actually measured data and label information, then segmentation accuracy is improved, but costs increase and data availability decreases
Solution Approach 1:
The patent creates duplicate layers that copy the structure and functionality of the original pre-trained model layers. These duplicate layers are then trained to adapt to the target domain distribution without requiring actual measured data with labels, thus achieving domain adaptation while avoiding the cost and availability issues of obtaining real labeled data.
Solution Approach 2:
The patent modifies the parameters of the duplicate layers during training to match the target domain distribution. By changing the parameters of the duplicate layers through training with domain adaptation loss, the model achieves accurate segmentation for the target domain without needing actual measured data with labels.
2Reliability
If domain distribution differences are addressed through fine-tuning with actual data, then segmentation performance is improved, but costs and data acquisition difficulty increase
Solution Approach 1:
The patent uses duplicate layers that copy the original model architecture, allowing the system to adapt to domain distribution differences without complex data acquisition processes. The duplicate layers are trained using synthesized or available data, avoiding the need for expensive and difficult-to-obtain actual measured data.
Solution Approach 2:
The duplicate layers act as an intermediary between the pre-trained model and the target domain application. They absorb the domain adaptation requirements, allowing the original pre-trained model to remain unchanged while the duplicate layers handle the domain-specific adjustments, simplifying the overall system complexity.
3Adaptability or versatility
If duplicate layers are trained to match target domain features, then domain adaptability is improved, but training complexity increases
Solution Approach 1:
The patent segments the model into original layers and duplicate layers, with each serving a specific function. The duplicate layers are specifically trained for domain adaptation while the original layers remain frozen. This segmentation allows focused training on only the necessary components, reducing overall training complexity despite the added adaptability.
Solution Approach 2:
The patent applies different training strategies to different parts of the model: the original layers are kept fixed while only the duplicate layers are trained. This local quality approach ensures that domain adaptation is achieved where needed without unnecessarily complicating the training of the entire model, thus balancing adaptability with training complexity.
Data Source
AI summary
A processor-implemented learning method for an image segmentation includes training first duplicate layers, as duplications of trained first layers of a pre-trained model, so that a second feature extracted from a target image by the trained first duplicate layers is matched to a first feature extracted from a training image by the trained first layers; regularizing the trained first duplicate layers so that a similarity between the first feature and a third feature extracted from the training image by the regularized first duplicate layers meets a threshold; and training second duplicate layers, as duplications of trained second layers of the pre-trained model, to be configured to segment the target image based on the regularized first duplicate layers, the trained second layer being configured to segment the training image.