A Survey on Deep Transfer Learning

Abstract

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.

arXiv commentThe 27th International Conference on Artificial Neural Networks (ICANN 2018)

References in corpus (10)

• Learning Transferable Features with Deep Adaptation Networks
• Deep Domain Confusion: Maximizing for Domain Invariance
• Conditional Adversarial Domain Adaptation
• Unsupervised Domain Adaptation with Residual Transfer Networks
• Adversarial Discriminative Domain Adaptation
• Domain-Adversarial Neural Networks
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Deep Transfer Learning with Joint Adaptation Networks
• Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

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• Face Detection with Feature Pyramids and Landmarks
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
• GPS-Denied Navigation Using Low-Cost Inertial Sensors and Recurrent Neural Networks