Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enorm...

詳細記述

書誌詳細

出版年:	Mathematics
主要な著者:	Mohammed Majid Abdulrazzaq, Nehad T. A. Ramaha, Alaa Ali Hameed, Mohammad Salman, Dong Keon Yon, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2024-03-01
主題:	deep learning (DL) self-supervised learning (SSL) machine learning (ML) cognition classification data annotation
オンライン･アクセス:	https://www.mdpi.com/2227-7390/12/5/758