Unsupervised Domain Adaptation Using Sliced-Wasserstein Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing transfer learning methods require human intervention and learning from scratch when adapting to new data distributions, especially in scenarios with abundant source data but sparse or unlabeled target data.
Innovation Solution
The system employs sliced-Wasserstein distance as a dissimilarity measure to align input data distributions between source and target domains, generating pseudo-labels for unlabeled target data to adapt a learned model without human intervention, enabling autonomous domain adaptation in unsupervised transfer learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional transfer learning methods are used to adapt models to new data distributions, then model adaptation capability is improved, but human intervention and retraining from scratch are required
Solution Approach 1:
The system enables models to automatically adapt to new data distributions by computing sliced-Wasserstein distances between source and target domain embeddings and self-adjusting parameters without requiring human intervention or retraining from scratch, making the adaptation process autonomous
Solution Approach 2:
The system changes model parameters based on computed sliced-Wasserstein distances between domain embeddings, allowing continuous adaptation to new data distributions by adjusting parameters rather than retraining entire models
2Measurement precision
If models are retrained from scratch for each new domain, then adaptation accuracy is improved, but computational time and resources increase
Solution Approach 1:
The system pre-computes sliced-Wasserstein distances between domain embeddings and prepares adaptation parameters in advance, allowing rapid deployment to new domains without time-consuming retraining processes while maintaining high adaptation accuracy
Solution Approach 2:
Instead of retraining models from scratch, the system efficiently adapts by changing specific parameters based on pre-computed sliced-Wasserstein distances, significantly reducing computational time while preserving adaptation accuracy
3Reliability
If sliced-Wasserstein distance is used to align data distributions, then domain adaptation performance is improved, but computational complexity increases
Solution Approach 1:
The system extracts and computes only the essential sliced-Wasserstein distances between domain embeddings rather than performing full distribution alignment, reducing computational complexity while maintaining reliable domain adaptation performance
Solution Approach 2:
The system uses sliced-Wasserstein distance computations to determine parameter adjustment amounts, providing a balanced approach that improves adaptation performance through targeted parameter changes without requiring full retraining or overly complex computational processes
Data Source
AI summary
Described is a system for unsupervised domain adaptation in an autonomous learning agent. The system adapts a learned model with a set of unlabeled data from a target domain, resulting in an adapted model. The learned model was previously trained to perform a task using a set of labeled data from a source domain. The set of labeled data has a first input data distribution, and the set of unlabeled target data has a second input data distribution that is distinct from the first input data distribution. The adapted model is implemented in the autonomous learning agent, causing the autonomous learning agent to perform the task in the target domain.