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Found 7 papers, 3 papers with code
Are Out-of-Distribution Detection Methods Reliable?
This paper establishes a novel evaluation framework for assessing the performance of out-of-distribution (OOD) detection in realistic settings.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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.
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.
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection
In particular, context autoencoders have been successful in the novelty detection task because of the more effective representations they learn by reconstructing original images from randomly masked images.
Ranked #28 on
Anomaly Detection
on One-class CIFAR-10
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition
While they are capable of reconstructing images that are visually appealing, the identity-related information is not preserved.
Latent Vector Recovery of Audio GANs
Through our deep neural network based method of training on real and synthesized audio, we are able to predict a latent vector that corresponds to a reasonable reconstruction of real audio.
Inverse mapping of face GANs
The vast majority of studies on latent vector recovery perform well only on generated images, we argue that our method can be used to determine a mapping between real human faces and latent-space vectors that contain most of the important face style details.
