Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI

TL;DR

AMDTL introduces a hybrid meta-learning and domain adaptation framework that significantly improves transferability, robustness, and efficiency of AI models across diverse and unseen domains, addressing key challenges like domain misalignment and catastrophic forgetting.

Contribution

This paper proposes a novel Adaptive Meta-Domain Transfer Learning framework combining meta-learning, adversarial training, and dynamic feature regulation for improved cross-domain AI transfer.

Findings

Outperforms existing transfer learning methods in accuracy

Enhances adaptation efficiency and robustness

Effectively mitigates negative transfer and catastrophic forgetting

Abstract

This paper presents Adaptive Meta-Domain Transfer Learning (AMDTL), a novel methodology that combines principles of meta-learning with domain-specific adaptations to enhance the transferability of artificial intelligence models across diverse and unknown domains. AMDTL aims to address the main challenges of transfer learning, such as domain misalignment, negative transfer, and catastrophic forgetting, through a hybrid framework that emphasizes both generalization and contextual specialization. The framework integrates a meta-learner trained on a diverse distribution of tasks, adversarial training techniques for aligning domain feature distributions, and dynamic feature regulation mechanisms based on contextual domain embeddings. Experimental results on benchmark datasets demonstrate that AMDTL outperforms existing transfer learning methodologies in terms of accuracy, adaptation efficiency, and robustness. This research provides a solid theoretical and practical foundation for the application of AMDTL in various fields, opening new perspectives for the development of more adaptable and inclusive AI systems.