24 citations · 25 across the 5 of their papers we have counts for
5 papers
Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels
Jae Soon Baik, In Young Yoon, Kun Hoon Kim +1
Deep neural networks have demonstrated remarkable advancements in various fields using large, well-annotated datasets. However, real-world data often exhibit long-tailed distributi…
DaDe: Delay-adaptive Detector for Streaming Perception
Wonwoo Jo, Kyungshin Lee, Jaewon Baik +3
Recognizing the surrounding environment at low latency is critical in autonomous driving. In real-time environment, surrounding environment changes when processing is over. Current…
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition
Jae Soon Baik, In Young Yoon, Jun Won Choi
There is growing interest in the challenging visual perception task of learning from long-tailed class distributions. The extreme class imbalance in the training dataset biases the…
Learning from Data with Noisy Labels Using Temporal Self-Ensemble
Jun Ho Lee, Jae Soon Baik, Tae Hwan Hwang +1
There are inevitably many mislabeled data in real-world datasets. Because deep neural networks (DNNs) have an enormous capacity to memorize noisy labels, a robust training scheme i…
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning
Jae Soon Baik, In Young Yoon, Jun Won Choi
Modern deep learning has achieved great success in various fields. However, it requires the labeling of huge amounts of data, which is expensive and labor-intensive. Active learnin…