4 papers
CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
HyunGi Kim, Jisoo Mok, Hyungyu Lee +2
Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in re…
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training
Saehyung Lee, Hyungyu Lee
Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness. However, there is no guarantee that it will always…
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks
Han Gyel Sun, Hyunjae Ahn, HyunGyu Lee +1
In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin…
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Saehyung Lee, Hyungyu Lee, Sungroh Yoon
Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against ad…