11 citations · 18 across the 4 of their papers we have counts for
7 papers
An Extendable, Efficient and Effective Transformer-based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun +5
Transformers have been widely used in numerous vision problems especially for visual recognition and detection. Detection transformers are the first fully end-to-end learning syste…
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park +3
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, h…
Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin +3
Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over tim…
Robust Learning by Self-Transition for Handling Noisy Labels
Hwanjun Song, Minseok Kim, Dongmin Park +2
Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize fa…
How does Early Stopping Help Generalization against Label Noise?
Hwanjun Song, Minseok Kim, Dongmin Park +1
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that su…
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Hwanjun Song, Minseok Kim, Sundong Kim +1
The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch s…