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. 2022;9(1):102.
doi: 10.1186/s40537-022-00652-w. Epub 2022 Oct 22.

Transfer learning: a friendly introduction

Asmaul Hosna #  1 , Ethel Merry #  1 , Jigmey Gyalmo #  1 , Zulfikar Alom  1 , Zeyar Aung  2 , Mohammad Abdul Azim  1
Affiliations

Transfer learning: a friendly introduction

Asmaul Hosna et al. J Big Data. 2022.

Abstract

Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Keywords: Domain adaptation; Image classification; Machine learning; Multi-task learning; Sample selection; Sentiment classification; Transfer learning; Zero shot translation.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Traditional/Classical ML vs. TL [3]
Fig. 2
Fig. 2
Changes brought by sample selection in TL
Fig. 3
Fig. 3
Symbols abbreviations [16]
Fig. 4
Fig. 4
The architecture of VGG-16 ConvNet [39]
Fig. 5
Fig. 5
Polarities of sentiment analysis [44]
Fig. 6
Fig. 6
Challenges and gaps in the literatures of TL concerning this table
See this image and copyright information in PMC

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