A Review of Deep Transfer Learning and Recent Advancements

学习迁移 计算机科学 深度学习 人工智能 遗忘 机器学习 任务(项目管理) 依赖关系(UML) 困境 工程类 哲学 语言学 系统工程 认识论
作者
Mohammadreza Iman,Hamid R. Arabnia,Khaled Rasheed
出处
期刊:Technologies (Basel) [Multidisciplinary Digital Publishing Institute]
卷期号:11 (2): 40-40 被引量:201
标识
DOI:10.3390/technologies11020040
摘要

Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. Moreover, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on specific adjustments and strategies for different scenarios. This paper reviews the concept, definition, and taxonomy of deep transfer learning and well-known methods. It investigates the DTL approaches by reviewing applied DTL techniques in the past five years and a couple of experimental analyses of DTLs to discover the best practice for using DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends.
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