Transfer Learning and its Role in Machine Learning

EasyChair Preprint 12570

Abstract

Transfer Learning has emerged as a prominent technique in machine learning, revolutionizing model training and deployment. By leveraging pre-trained models and knowledge from related tasks or domains, Transfer Learning enhances the performance of target tasks with limited data. This abstract provides an overview of Transfer Learning and its role in machine learning.

The abstract highlights the benefits of Transfer Learning, including faster training, reduced data requirements, improved generalization, and better convergence. It explores the diverse applications of Transfer Learning in domains such as image classification, object detection, natural language processing, recommendation systems, healthcare, and robotics.

The abstract also acknowledges the challenges and considerations in applying Transfer Learning, such as task similarity, data mismatch, overfitting, labeling efforts, generalization vs. specialization, computational resources, ethical considerations, and task-specific tuning. It emphasizes the importance of addressing these challenges to ensure effective and ethical use of Transfer Learning.

Furthermore, the abstract presents future directions and research trends in Transfer Learning. These include advancements in unsupervised and self-supervised learning, multi-task learning, domain generalization, zero-shot learning, lifelong and continual learning, meta-learning, cross-modal Transfer Learning, explainability and interpretability, and robustness against adversarial attacks.

Keyphrases: Advancement, Robotics, Technology

@booklet{EasyChair:12570,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Transfer Learning and its Role in Machine Learning},
  howpublished = {EasyChair Preprint 12570},
  year      = {EasyChair, 2024}}