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Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection
YeongHyeon Park, Myung Jin Kim, Hyeong Seok Kim
A pre-trained visual-language model, contrastive language-image pre-training (CLIP), successfully accomplishes various downstream tasks with text prompts, such as finding images or…
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
YeongHyeon Park, Sungho Kang, Myung Jin Kim +2
In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt larg…
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy
YeongHyeon Park, Sungho Kang, Myung Jin Kim +3
Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for traini…
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification
YeongHyeon Park, Sungho Kang, Myung Jin Kim +4
Unsupervised anomaly detection (UAD) is a widely adopted approach in industry due to rare anomaly occurrences and data imbalance. A desirable characteristic of an UAD model is cont…