Edge Device Prompt Learning for Label-Free Domain Adaptation
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Solution Overview
Problem
Existing machine learning models struggle to adapt to new domains without extensive retraining or manual data labeling, particularly in edge devices operating in dynamic environments, leading to performance degradation due to domain shift.
Innovation Solution
The use of unsupervised domain adaptation techniques leveraging prompt learning, such as Domain Adaptation via Prompt Learning (DAPL), which involves detecting domain shifts, generating pseudo labels, and updating models using image-to-text descriptions and contrastive learning to align image and text representations in a shared feature space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised learning techniques are used to address domain shift, then model performance can be maintained, but labeled data from the new domain is required which is costly and time-consuming to obtain
Solution Approach 1:
The system performs unsupervised domain adaptation where the model automatically learns to adapt to new domains without requiring manual labeling. The unsupervised learning algorithm autonomously identifies domain characteristics and adjusts model parameters, eliminating the need for human annotators to label data while maintaining model performance.
Solution Approach 2:
The patent introduces an intermediary representation space that bridges the source domain and target domain. By learning a shared representation that captures essential features without domain-specific labels, the system enables transfer of knowledge between domains without requiring labeled data in the target domain, thus resolving the time-consuming labeling requirement.
2Reliability
If models are trained on large labeled datasets, then initial performance is achieved, but adaptation to new domains requires extensive retraining which increases computational cost
Solution Approach 1:
The system performs preliminary learning of domain-invariant representations during the initial training phase. By pre-learning features that are useful across multiple domains and using unsupervised techniques to capture domain characteristics without labels, the system reduces the need for extensive retraining when encountering new domains, thereby lowering computational costs.
Solution Approach 2:
The patent employs dynamic adaptation mechanisms where the model can adjust its parameters and representations based on the characteristics of new domains encountered during deployment. This dynamic behavior allows the model to adapt to new domains using minimal computational resources compared to static retraining approaches, as the model learns to identify and adapt to domain shifts in real-time.
3Adaptability or versatility
If models encounter data differing significantly from training data, then real-world applicability is achieved, but performance decreases due to domain shift
Solution Approach 1:
The system changes the parameter representation by learning domain-specific parameters that capture the essential characteristics of different domains. By representing domains through learned parameters rather than requiring complete retraining, the model can adapt to new domains and maintain performance when encountering data that differs significantly from training data, thus resolving the domain shift problem.
Data Source
AI summary
Techniques are disclosed for unsupervised domain adaptation using prompt learning in edge devices. An example system includes a memory having instructions, and a processor communicatively coupled to the memory and configured to execute the instructions. Example instructions include: comparing statistics of data samples collected from an edge device against a plurality of known domain statistics to detect a new domain; using descriptions generated for the collected data samples to determine a pseudo label associated with the new domain, where the pseudo label is generated using unsupervised machine learning; and applying a domain adaptation process using prompt learning based on the new domain and on the associated pseudo label to generate new prompts usable with a machine learning multimodal model for the new domain, and to update the known domain statistics to include statistics of the new domain, where the multimodal model is trained on text similarity.