no code implementations • ICCV 2023 • Joonhyung Park, Hyunjin Seo, Eunho Yang
Understanding point clouds captured from the real-world is challenging due to shifts in data distribution caused by varying object scales, sensor angles, and self-occlusion.
1 code implementation • 3 Jun 2023 • Changhun Kim, Joonhyung Park, Hajin Shim, Eunho Yang
Automatic speech recognition (ASR) models are frequently exposed to data distribution shifts in many real-world scenarios, leading to erroneous predictions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 26 Jun 2022 • Jaeyun Song, Joonhyung Park, Eunho Yang
Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes.
no code implementations • 16 Dec 2021 • Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang
However, they now suffer from lack of sample diversification as they always deterministically select regions with maximum saliency, injecting bias into the augmented data.
no code implementations • 10 Nov 2021 • Joonhyung Park, Hajin Shim, Eunho Yang
Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult.
no code implementations • ICLR 2022 • Joonhyung Park, Jaeyun Song, Eunho Yang
In many real-world node classification scenarios, nodes are highly class-imbalanced, where graph neural networks (GNNs) can be readily biased to major class instances.