19 papers with code • 0 benchmarks • 3 datasets
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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.
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
We emphasize the relevance of OOD 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.
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.
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
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.