21 papers with code • 0 benchmarks • 3 datasets
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Most implemented papers
Adversarially Learned One-Class Classifier for Novelty Detection
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
We emphasize the relevance of OODD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems.
Automatic support vector data description
Event handlers have wide range of applications such as medical assistant systems and fire suppression systems.
Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier
Specifically, we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene.
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
We assume that training data is available to describe only the inlier distribution.
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions.