Bridging Domains with Transfer Learning: Strategies for Multi-Task and Multi-Dataset Finetuning
Tehseen Ullah, Steven Davy, John D. Kelleher · Artificial intelligence · 2025
The chapter explores the innovative integration of transfer learning with multi-dataset and multi-task fine-tuning to build versatile, resource-efficient models for computer vision-related tasks. It delves into the paradigm shift from traditional single-task training, where models are optimized for isolated datasets to a unified multi-task framework where a shared backbone supports diverse, domain-specific tasks. By leveraging pretrained models and implementing different multi-datasets and multi-tasks fine-tuning techniques such as mixture of experts, model merging, adaptive multi-task learning and modular architectures for multi-dataset training techniques, the discussed approaches enhance performance across varied datasets as well as significantly reduce computational cost and training time. This chapter addresses challenges such as data imbalance, conflicting gradients and sensitivity to merging coefficients, outlining how modern transfer learning techniques can reconcile these issues to produce scalable models that are both efficient and adaptable to real-world scenarios.