Transfer learning using computational intelligence: A survey

By Jie Lu, Vahid Behbood, Peng Hao, Hua Zuo, Shan Xue, Guangquan Zhang. Knowledge-Based Systems, 2015

896 citations recorded by the source corpus.

Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, computational intelligence has recently been applied in transfer learning. This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: (a) neural network-based transfer learning; (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly support researchers and practice-based professionals to understand the developments in computational intelligence-based transfer learning research and applications.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/66062f08350fa0bea1101ea774577d4f1eb3065d

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In Artificial Intelligence

Citations in the corpus

Remarks on multi-output Gaussian process regression (2018, background): “The study of MOGP has a long history and is known as multivariate Kriging or Co-Kriging [16, 17, 18, 19] in the geostatistic community; it also overlaps with the broad field of multi-task learning [20, 21] and transfer learning [22, 23] of the machine learning community.”
A survey of the applications of text mining in financial domain (2016, background): “Lu et al. (2015) presented a survey paper on Transfer learning.”
Deep Model Based Domain Adaptation for Fault Diagnosis (2017, background): “Many examples in knowledge engineering areas have been found where domain adaptation can be truly beneficial and promising, including image classification, natural language processing, object recognition, and feature learning [2], [3].”
Investigation of transfer learning for ASR using LF-MMI trained neural networks (2017, background): “There is a rich survey of transfer learning methods in the literature [1, 2, 3].”
Learning Bounds for Open-Set Learning (2021, methodology): “Based on this fact, we aim to establish the OSL theory from transfer learning (Dong et al., 2019; 2020a;b; 2021; Liu et al., 2019; Lu et al., 2015; Luo et al., 2020a; Niu et al., 2020; Pan & Yang, 2010; Wang et al., 2020), which learns knowledge for a given domain from a different, but relative doma”
Deep learning for smart fish farming: applications, opportunities and challenges (2020, methodology): “These models are then adapted to the specific challenges and datasets (Lu et al., 2015).”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “y=1 ey weights of models trained on different datasets are transferred to the new model [45, 46].”
Cross-Domain Detection of Abusive Language Online (2018, background): “This idea is well established in the machine learning community under the name of transfer learning; we refer to (Weiss et al., 2016; Lu et al., 2015) for overviews.”
Artificial intelligence in recommender systems (2020, background): “Transfer learning techniques can be divided into three main categories [41].”