One-class classifier
24 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in One-class classifier
COVID-19-CT-CXR
Simulated micro-Doppler Signatures
Latest papers with no code
Beyond the Known: Adversarial Autoencoders in Novelty Detection
The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution.
One-class anomaly detection through color-to-thermal AI for building envelope inspection
We present a label-free method for detecting anomalies during thermographic inspection of building envelopes.
Lp-Norm Constrained One-Class Classifier Combination
The vector-norm constraint enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble in the space of base learners and acts as a (soft) classifier selection mechanism by shaping the relative magnitudes of fusion weights.
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
Furthermore, we conducted comparative experiments to validate the superiority of combining image features and timing characteristics within PUPGUARD for enhancing resistance against puppet attacks.
Anomaly detection for automated inspection of power line insulators
Inspection of insulators is important to ensure reliable operation of the power system.
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems.
Morse Neural Networks for Uncertainty Quantification
We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points.
A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images
For this reason, several detectors have been developed providing excellent performance in computer vision applications, however, they can not be applied as they are to multispectral satellite images, and hence new models must be trained.
Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity
(4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.
An Upper Bound for the Distribution Overlap Index and Its Applications
In domain shift analysis, we propose a theorem based on our bound.
