This work studies the bias issue of pseudo-labeling, a natural phenomenon that widely
occurs but often overlooked by prior research. Pseudo-labels are generated when a …

Diffusion-based models have shown the merits of generating high-quality visual data while
preserving better diversity in recent studies. However, such observation is only justified with …

Abstract Domain generalization-based fault diagnosis (DGFD) has garnered significant
attention due to its ability to generalize prior diagnostic knowledge to unseen working …

The data imbalance problem extensively exists in wind turbine fault diagnosis, resulting in
the compromise between learning attention to majority and minority classes. In this paper, a …

Recent studies have pointed out that many well-developed Visual Question Answering
(VQA) models are heavily affected by the language prior problem. It refers to making …

Online continual learning suffers from an underfitted solution due to insufficient training for
prompt model updates (eg single-epoch training). To address the challenge we propose an …

In this article, a new fault diagnosis problem is formulated, which involves a large number of
normal samples and in which almost all the fault classes are few-shot classes. Although this …

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced
Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused …

Existing research on unsupervised domain adaptation (UDA) has primarily centered on
mitigating differences in feature distribution across various domains. However, in real-world …

In this research, we propose a deep learning-based approach for disruption prediction in
KSTAR using In-vessel Visible Inspection System (IVIS) video and 0D parameters. Here …