Interactive Augmentations, Features, and Parameters for Contrastive Learning [AI-eXplained]

Yu Ting Chen, Chien Yu Chiou, Chun Rong Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Recently, contrastive learning has shown its effectiveness in self-supervised learning by training features of augmentations of input images based on the contrastive loss. This paper aims to introduce contrastive learning and discuss the effects of augmentations, features, and parameters of contrastive learning. Interactive figures are developed to demonstrate the effective schemes proposed in contrastive learning and can be accessed on IEEE Xplore.

Original languageEnglish
Pages (from-to)79-80
Number of pages2
JournalIEEE Computational Intelligence Magazine
Volume19
Issue number1
DOIs
StatePublished - 1 Feb 2024

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