Deep One-Class Classification via Interpolated Gaussian Descriptor

@inproceedings{Chen2022DeepOC,
  title={Deep One-Class Classification via Interpolated Gaussian Descriptor},
  author={Yuanhong Chen and Yu Tian and Guansong Pang and G. Carneiro},
  booktitle={AAAI},
  year={2022}
}
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian… 

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References

SHOWING 1-10 OF 83 REFERENCES
Adversarially Learned One-Class Classifier for Novelty Detection
TLDR
The results on MNIST and Caltech-256 image datasets, along with the challenging UCSD Ped2 dataset for video anomaly detection illustrate that the proposed method learns the target class effectively and is superior to the baseline and state-of-the-art methods.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
TLDR
This work introduces a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space and shows the model efficacy and superiority over previous state-of-the-art approaches.
Deep One-Class Classification
TLDR
This paper introduces a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective and shows the effectiveness of the method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
TLDR
A novel model called OCGAN is presented for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class using a de-noising auto-encoder network.
Learning and Evaluating Representations for Deep One-class Classification
TLDR
A novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations and argues that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches.
Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection
TLDR
This work presents a Semantic Context based Anomaly Detection Network, SCADN, for unsupervised anomaly detection by learning the semantic context from the normal samples by generating multi-scale striped masks and training a generative adversarial network to reconstruct the unseen regions.
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
TLDR
This work introduces an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain, and hence detect abnormality based on deviation from this model.
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
TLDR
This work introduces the MVTec Anomaly Detection (MVTec AD) dataset containing 5354 high-resolution color images of different object and texture categories, and conducts a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pre-trained convolved neural networks.
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
TLDR
This work proposes a two-stage framework for building anomaly detectors using normal training data only, which first learns self-supervised deep representations and then builds a generative one-class classifier on learned representations.
Learning Deep Features for One-Class Classification
TLDR
A novel deep-learning-based approach for one-class transfer learning in which labeled data from an unrelated task is used for feature learning in one- class classification and achieves significant improvements over the state-of-the-art.
...
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