计算机科学
学习迁移
水准点(测量)
人工智能
模式识别(心理学)
上下文图像分类
生成语法
生成对抗网络
图像(数学)
领域(数学分析)
机器学习
集合(抽象数据类型)
任务(项目管理)
对抗制
集成学习
数学
数学分析
管理
大地测量学
经济
程序设计语言
地理
作者
Xinyue Wang,Jun Jiang,Mingliang Gao,Zheng Liu,Chengyuan Zhao
标识
DOI:10.1117/1.jei.30.1.013016
摘要
Transfer learning provides a useful solution to learn a new conceptual domain from few examples, which exploits prior knowledge from a related domain. We proposed a simple and yet effective transfer learning method for image classification that constructs an activation ensemble generative adversarial net (AE-GAN) to transfer knowledge from one dataset to another. The AE-GAN is mainly composed of three convolutional layers and adopts an ensemble of multiple activation functions. Experimental results on five benchmark datasets show that when only a few samples are available for training a target task, leveraging datasets from other related datasets by AE-GAN can significantly improve the performance for image classification with a small set of samples.
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