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[開學]106學年第1學期的課程看版開張了 歡迎同學問問題-20170917

作者 主題: Neurocomputing-Special Issue on Deep Learning with Small Samples-20200415  (閱讀 265 次)

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https://www.journals.elsevier.com/neurocomputing/call-for-papers/special-issue-on-deep-learning-with-small-samples

Important Dates
Submission portal open date: 15 Oct. 2019
Submission Deadline: 15 Apr. 2020
First Review Decision: 15 Aug. 2020
Revisions Due: 15 Oct. 2020
Final Manuscript: 15 Jan. 2021

1. Summary and Scope

In machine learning and computer vision fields, due to the rapid development of deep learning, recent years have witnessed breakthroughs for large-sample classification tasks. However, it remains a persistent challenge to learn a deep neural network with good generalizability from only a small number of training samples. In fact, humans can easily learn the concept of a class from a small amount of data rather than from millions of data. Moreover, many types of real-world data are small in quantity and are expensive to collect and label. Motivated by this fact, research on deep learning with small samples becomes more and more prevalent in the communities of machine learning and computer vision, for example, researches focusing on one-shot classification, few-shot classification, as well as classification with small training samples.
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