39 citations · 52 across the 4 of their papers we have counts for
5 papers
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning
Jouwon Song, Kyeongbo Kong, Ye-In Park +2
Anomaly segmentation, which localizes defective areas, is an important component in large-scale industrial manufacturing. However, most recent researches have focused on anomaly de…
Mitigating Memorization in Sample Selection for Learning with Noisy Labels
Kyeongbo Kong, Junggi Lee, Youngchul Kwak +3
Because deep learning is vulnerable to noisy labels, sample selection techniques, which train networks with only clean labeled data, have attracted a great attention. However, if t…
Core-set Sampling for Efficient Neural Architecture Search
Jae-hun Shim, Kyeongbo Kong, Suk-Ju Kang
Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models. However, the m…
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation
Jou Won Song, Kyeongbo Kong, Ye In Park +1
Anomaly detection is a task that recognizes whether an input sample is included in the distribution of a target normal class or an anomaly class. Conventional generative adversaria…
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback
Kyeongbo Kong, Woo-Jin Song, Moonsik Min
We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed…