Domain Adaptation Using Weakly Supervised Target-Irrelevant Data
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Solution Overview
Problem
Current domain adaptation and fusion methods require task-relevant data from the target domain, which is often difficult to obtain, especially in machine learning applications like computer vision and document classification, leading to poor performance due to domain shift and limited data availability.
Innovation Solution
The method involves transferring learned abstract features from a source domain to a target domain using task-irrelevant data pairs, optimizing over the target objective without requiring task-relevant data from the target domain, and fusing source and target modalities using simulated target inputs to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain adaptation is performed using conventional methods requiring task-relevant target domain data, then classification accuracy can be improved, but data acquisition cost and complexity increase significantly
Solution Approach 1:
The patent extracts and utilizes only the task-irrelevant portions of target domain data, separating them from the required task-relevant data. By focusing solely on irrelevant data pairs (images without annotations), the method eliminates the need for costly task-relevant labeled data while still achieving domain adaptation through feature alignment on shared irrelevant features.
Solution Approach 2:
The patent introduces task-irrelevant data as an intermediary medium for domain adaptation. Instead of directly using task-relevant labeled data from the target domain, the method uses irrelevant data pairs as a mediator to learn domain-invariant features, which then transfer knowledge to improve task-relevant classification performance.
2Ease of manufacture
If domain adaptation is performed without task-relevant target domain data, then data acquisition cost is reduced, but classification performance deteriorates due to domain shift
Solution Approach 1:
The patent changes the parameter of data utilization from requiring task-relevant labeled data to using only task-irrelevant unlabeled data. By altering the data parameter from annotated to unannotated and from relevant to irrelevant, the method reduces acquisition cost while maintaining adaptation effectiveness through domain invariant feature learning.
Solution Approach 2:
The patent makes task-irrelevant data serve multiple functions: it acts as both the training data for domain adaptation and as a means to learn domain-invariant features. This universal use of irrelevant data eliminates the need for separate task-relevant labeled data while still achieving accurate classification.
3Reliability
If multiple domains are fused to improve robustness, then noise resistance increases, but system complexity and computational cost increase
Solution Approach 1:
The patent merges source domain and target domain data into a unified training framework using irrelevant data pairs. By combining both domains' irrelevant data and applying consistent domain invariant feature learning, the method achieves robust multi-domain performance while maintaining a relatively simple unified system architecture.
4Measurement precision
If deep neural networks are trained with large amounts of labeled data, then classification performance is improved, but data annotation time and cost increase
Solution Approach 1:
The patent extracts and removes the annotation requirement from the training process by utilizing only task-irrelevant unlabeled data. By taking out the need for time-consuming annotations and focusing on unannotated irrelevant data, the method achieves performance improvement without incurring annotation time costs.
Data Source
AI summary
Aspects include receiving a request to perform an image classification task in a target domain. The image classification task includes identifying a feature in images in the target domain. Classification information related to the feature is transferred from a source domain to the target domain. The transferring includes receiving a plurality of pairs of task-irrelevant images that each includes a task-irrelevant image in the source domain and in the target domain. The task-irrelevant image in the source domain has a fixed correspondence to the task-irrelevant image in the target domain. A target neural network is trained to perform the image classification task in the target domain. The training is based on the plurality of pairs of task-irrelevant images. The image classification task is performed in the target domain and includes applying the target neural network to an image in the target domain and outputting an identified feature.