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Found 2 papers, 1 papers with code
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.
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
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