Papers Supervised Defect Detection
“Supervised Defect Detection” 태그가 달린 논문 8편 · 필터 해제
Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering
In the realm of industrial quality inspection, defect detection stands as a critical component, particularly in high-precision, safety-critical sectors such as automotive components aerospace, and medical devices. Tradit…
Defect DetectionSupervised Defect DetectionISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets tha…
Anomaly DetectionDefect DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly Detection+3SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to f…
Anomaly DetectionDefect DetectionSupervised Defect DetectionDSR -- A dual subspace re-projection network for surface anomaly detection
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution a…
Anomaly DetectionAnomaly LocalizationDefect DetectionSupervised Defect Detection+2Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often…
Anomaly DetectionContrastive LearningSupervised Anomaly DetectionSupervised Defect DetectionCatching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world applications, such as defect samples identifi…
Anomaly DetectionSupervised Anomaly DetectionSupervised Defect DetectionMixed supervision for surface-defect detection: from weakly to fully supervised learning
Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-pr…
Anomaly DetectionDefect DetectionSupervised Defect DetectionWeakly Supervised Defect DetectionSame Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their …
Anomaly DetectionDefect DetectionSupervised Defect Detection