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Found 4 papers, 2 papers with code
Deep Visual Anomaly detection with Negative Learning
However, these are trained with only normal data and at the test time, given abnormal data as input, may often generate normal-looking output.
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels.
SmoothMix: A Simple Yet Effective Data Augmentation to Train Robust Classifiers
Data augmentation has been proven effective which, by preventing overfitting, can not only enhances the performance of a deep neural network but also leads to a better generalization even with limited dataset.
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
Another possible approach is to use both generator and discriminator for anomaly detection.
Ranked #1 on
Anomaly Detection
on MNIST-test
