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Found 6 papers, 3 papers with code
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
We propose Class-Agnostic Distribution Alignment (CADA) to align the mismatched score distribution of each implicit class without knowing class information, which enables unified anomaly detection for all classes and samples.
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions.
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e. g. ImageNet.
Collective Knowledge Graph Completion with Mutual Knowledge Distillation
Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years.
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision.
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution
High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations.
