Manuscript Details - IJARW2190

ManuScript Details
Paper Id: IJARW2190
Title: OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION
Published in: International Journal Of All Research Writings
Publisher: IJARW
ISSN: 2582-1008
Volume / Issue: Volume 6 Issue 1
Pages: 7
Published On: 7/10/2024 1:37:52 AM      (MM/dd/yyyy)
Main Author Details
Name: Trang Phung T. Thu
Institute: School of Foreign Languages
Co - Author Details
Author Name Author Institute
Duong Ngoc Khang School of Foreign Languages
Abstract
Research Area: Artificial Intelligence
KeyWord: Deep learning, Imbalance classification, Long-tailed classification
Abstract: In recent years, the problem of data imbalance has become a major challenge, significantly impacting the process of mining information from data. This often happens when some classes have significantly larger samples than other classes. With the development of deep learning, there have been significant advances in representing and understanding information from images. However, when applying deep learning to practical image recognition tasks, the problem of "deep long-tail" becomes apparent. Training models to face even rare cases helps create robust and flexible models that are able to adapt well to real-world data fluctuations. This paper aims to comprehensively analyze the long-tail problem in image recognition, summarize the highlights and limitations of previous methods, and provide a view on future research directions.
Citations
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IEEE
Trang Phung T. Thu, Duong Ngoc Khang, "OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION", International Journal Of All Research Writings, vol. 6, no. 1, pp. 14-20, 2024.
MLA Trang Phung T. Thu, Duong Ngoc Khang "OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION." International Journal Of All Research Writings, vol 6, no. 1, 2024, pp. 14-20.
APA Trang Phung T. Thu, Duong Ngoc Khang (2024). OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION. International Journal Of All Research Writings, 6(1), 14-20.
OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION
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OVERVIEW ON STATE-OF-THE-ART DEEP LEARNING-BASED MODELS FOR IMBALANCE CLASSIFICATION

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