Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI
It addresses domain misalignment and negative transfer in transfer learning, offering a novel hybrid framework for more adaptable AI systems.
This paper tackled the problem of improving AI model transferability across diverse domains by proposing Adaptive Meta-Domain Transfer Learning (AMDTL), which outperformed existing methods in accuracy, adaptation efficiency, and robustness on benchmark datasets.
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.