Source-Free Active Domain Adaptation Through Guided Attention Transfer
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Solution Overview
Problem
Current active domain adaptation systems are not scalable when the source domain data is unavailable, particularly in scenarios where there is a shift in label space and lack of access to source data post-training, and they are task-specific, failing to adapt well to different classification categories or segmentation classes.
Innovation Solution
A deep-learning based guided attention transfer network generates attention maps from source and target network features, enabling parameter updates in the target network using ground truth label data, allowing source-free active domain adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current active domain adaptation systems are used for task-specific classification, then classification accuracy is improved, but scalability to different classification categories and segmentation classes deteriorates
Solution Approach 1:
The patent implements a universal domain adaptation framework that can handle multiple tasks including classification, detection, and segmentation through a unified attention transfer mechanism. The system uses a task-agnostic architecture where the guided attention transfer network can be applied across different visual tasks without task-specific customization, enabling the same core methodology to serve multiple functions.
Solution Approach 2:
The patent segments the domain adaptation process into distinct modular components: feature extraction from source and target networks, attention map generation through guided attention transfer, and parameter updates based on attention weights. This modular segmentation allows the system to be flexibly applied to different tasks by configuring which modules are needed, improving scalability across classification, detection, and segmentation tasks.
2Measurement precision
If large amounts of annotated source data are required for training, then model performance is improved, but data availability and system scalability deteriorate
Solution Approach 1:
The patent extracts and transfers only the essential knowledge from source domain data through attention maps, rather than requiring large amounts of annotated source data. The guided attention transfer mechanism identifies and extracts relevant feature relationships from source network features, transferring only the necessary information to the target network. This extraction approach reduces dependency on large annotated datasets while maintaining model performance.
Solution Approach 2:
The patent introduces attention maps as an intermediary that mediates knowledge transfer between source and target domains. Instead of directly using large amounts of annotated source data, the system uses attention maps generated from source network features to guide the transfer process. This intermediary mechanism enables efficient knowledge transfer with reduced data requirements, as the attention maps encode the essential transferable knowledge in a compact form.
3Reliability
If source data is unavailable after pre-training, then data privacy and storage requirements are improved, but domain adaptation capability deteriorates
Solution Approach 1:
The patent performs preliminary domain adaptation by generating and storing attention maps from source network features before source data becomes unavailable. The system pre-computes the attention mechanisms that capture the essential transferable knowledge, allowing subsequent target network training to proceed without access to original source data. This preliminary action ensures domain adaptation capability is established while maintaining data privacy and reducing storage requirements.
Solution Approach 2:
The patent creates a copy of the essential knowledge from source domain through attention maps, which can be stored and reused without requiring the original source data. The attention maps serve as a compressed representation that captures the critical transferable information, enabling the system to maintain domain adaptation capability while eliminating dependency on large amounts of stored source data, thus improving both privacy and storage efficiency.
Data Source
AI summary
Systems and methods for machine learning are described. The systems and methods include receiving target training data including a training image and ground truth label data for the training image, generating source network features for the training image using a source network trained on source training data, generating target network features for the training image using a target network, generating at least one attention map for training the target network based on the source network features and the target network features using a guided attention transfer network, and updating parameters of the target network based on the attention map and the ground truth label data.