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Found 3 papers, 1 papers with code
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD Detection
This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Augment to Detect Anomalies with Continuous Labelling
Therefore, training a regressor on these augmented samples will result in more separable distributions of labels for normal and real anomalous data points.
Anomaly Detection with Adversarially Learned Perturbations of Latent Space
The Adversarial Distorter is a convolutional encoder that learns to produce effective perturbations and the autoencoder is a deep convolutional neural network that aims to reconstruct the images from the perturbed latent feature space.
