A transformed-feature-space data augmentation method for defect segmentation

Niu, Shuanlong; Peng, Yaru; Li, Bin; Wang, Xinggang

doi:10.1016/j.compind.2023.103860

Cited by 11 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The manual aggregation of domain‐specific training datasets can be a labor‐intensive and costly endeavor. This challenge underscores the critical need for an exploration into data augmentation strategies, which has secured a broad footprint in the realm of image processing, typically implemented through transformations (Alqudah et al, 2023; Niu et al, 2023; Shorten & Khoshgoftaar, 2019; Xiang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Will sentiment analysis need subculture? A new data augmentation approach

Wang,

He,

et al. 2024

Asso for Info Science & Tech

View full text Add to dashboard Cite

Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture‐based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.

show abstract