Zero-Shot Deep Domain Adaptation With Common Representation Learning.

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Domain Adaptation (DA) seeks to transfer knowledge from a source domain to a target domain.
• Existing DA methods often require labeled target-domain data, limiting their applicability.
• A key challenge is adapting models when target domain data is scarce or unavailable.

Purpose of the Study:

• To introduce Zero-Shot Deep Domain Adaptation (ZDDA), a novel approach that eliminates the need for task-relevant target-domain training data.
• To develop ZDDA variants for classification (ZDDA-C) and metric learning (ZDDA-ML) tasks.
• To demonstrate ZDDA's applicability to both closed-set and open-set classification problems.

Main Methods:

• ZDDA learns to generate common, domain-invariant representations from paired, dual-domain, task-irrelevant data.
• Two variants, ZDDA-C and ZDDA-ML, are proposed for classification and metric learning, respectively.
• The generated representations are used to train models applicable across domains or in sensor fusion settings.

Main Results:

• ZDDA successfully adapts models without requiring task-relevant target-domain data.
• ZDDA-C demonstrates effectiveness in open-set classification scenarios.
• ZDDA-ML validates the approach's utility beyond classification tasks.
• Experimental results show ZDDA outperforms baseline methods under most conditions.

Conclusions:

• ZDDA offers a powerful solution for domain adaptation challenges where target data is limited.
• The method's flexibility extends to various tasks, including classification and metric learning, and open-set scenarios.
• ZDDA significantly advances the field by enabling effective knowledge transfer in zero-shot settings.