no code implementations • 15 Apr 2024 • Juhwan Choi, Jungmin Yun, Kyohoon Jin, Youngbin Kim
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models.
no code implementations • 29 Mar 2024 • Juhwan Choi, Youngbin Kim
Our experimental results highlight the possibility of curriculum data augmentation for image data.
no code implementations • 29 Mar 2024 • Juhwan Choi, Youngbin Kim
In the field of text data augmentation, rule-based methods are widely adopted for real-world applications owing to their cost-efficiency.
no code implementations • 22 Mar 2024 • Kyohoon Jin, Junho Lee, Juhwan Choi, Sangmin Song, Youngbin Kim
Inspired by recent studies on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models.
no code implementations • 20 Mar 2024 • Seunguk Yu, Juhwan Choi, Youngbin Kim
Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Eunju Lee, Kyohoon Jin, Youngbin Kim
However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song, Youngbin Kim
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity.
1 code implementation • 8 Feb 2024 • Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song, Youngbin Kim
Text data augmentation is a complex problem due to the discrete nature of sentences.
no code implementations • 2 Sep 2019 • Hee-Sun Choi, Junmo An, Jin-Gyun Kim, Jae-Yoon Jung, Juhwan Choi, Grzegorz Orzechowski, Aki Mikkola, Jin Hwan Choi
The aim of the framework is to generate a well-trained meta-model of multibody dynamics (MBD) systems.