4 citations · 6 across the 7 of their papers we have counts for
7 papers
Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng +2
Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. Fo…
Memory-Distilled Selection for Noise-Robust Anomaly Detection
Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4
Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is…
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon +4
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class dis…
Face Reconstruction Transfer Attack as Out-of-Distribution Generalization
Yoon Gyo Jung, Jaewoo Park, Xingbo Dong +3
Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penet…
Nearest Neighbor Guidance for Out-of-Distribution Detection
Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh
Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD det…
Periocular Embedding Learning with Consistent Knowledge Distillation from Face
Yoon Gyo Jung, Jaewoo Park, Cheng Yaw Low +3
Periocular biometric, the peripheral area of the ocular, is a collaborative alternative to the face, especially when the face is occluded or masked. However, in practice, sole peri…
Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection
Jaewoo Park, Yoon Gyo Jung, Andrew Beng Jin Teoh
In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to…