Schema Augmentation Improves Zero-Shot Domain Adaptation for End-to-End Dialogue State Tracking with Large Language Models
Schema Augmentation, a novel data augmentation technique, significantly improves the ability of large language models to perform dialogue state tracking in unseen domains without requiring target domain data during training.
Unified Language-driven Zero-shot Domain Adaptation: A Practical Approach for Robust Segmentation across Diverse Scenarios
A novel framework for Unified Language-driven Zero-shot Domain Adaptation (ULDA) that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge, by leveraging hierarchical context alignment, domain-consistent representation learning, and text-driven feature rectification.
ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer
Deep learning models can benefit from zero-shot domain adaptation using diffusion-based image transfer, as demonstrated by ZoDi.
Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer
Zero-shot domain adaptation method ZoDi utilizes diffusion models for image transfer and model adaptation, showing improved segmentation performance.
© 2024 by Linnk AI