Task guided representation learning using compositional models for zero-shot domain adaptation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Zero-shot domain adaptation (ZDA) aims to transfer knowledge from a source domain to a target domain lacking labeled data.
• Existing ZDA methods face challenges in learning effective feature representations that are invariant across domains.

Purpose of the Study:

• To develop a novel method for ZDA that learns domain-invariant and shareable feature representations tailored to specific task characteristics.
• To enable effective knowledge transfer in ZDA without relying on synthetic data or estimated target domain representations.

Main Methods:

• Proposes task-guided ZDA (TG-ZDA), utilizing multi-branch deep neural networks.
• Trains TG-ZDA models end-to-end to exploit feature invariance and shareability properties across domains.
• Evaluates the method on benchmark ZDA tasks using image classification datasets.

Main Results:

• TG-ZDA demonstrates superior performance compared to state-of-the-art ZDA methods.
• Achieves improved results across various domains and tasks in ZDA benchmarks.
• Effectively learns domain-invariant feature representations for enhanced cross-domain transfer.

Conclusions:

• The proposed TG-ZDA method offers a significant advancement in zero-shot domain adaptation.
• Task-guided feature learning enhances the robustness and generalizability of ZDA models.
• TG-ZDA provides a more efficient and effective approach for cross-domain knowledge transfer without target domain data.