Corpus ID: 236034173

Contrastive Predictive Coding for Anomaly Detection

@article{Haan2021ContrastivePC,
  title={Contrastive Predictive Coding for Anomaly Detection},
  author={Puck de Haan and Sindy L{\"o}we},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.07820}
}
Reliable detection of anomalies is crucial when deploying machine learning models in practice, but remains challenging due to the lack of labeled data. To tackle this challenge, contrastive learning approaches are becoming increasingly popular, given the impressive results they have achieved in self-supervised representation learning settings. However, while most existing contrastive anomaly detection and segmentation approaches have been applied to images, none of them can use the contrastive… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 27 REFERENCES
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. Expand
Deep Semi-Supervised Anomaly Detection
TLDR
This work presents Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection, and introduces an information-theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy the anomalous distribution, which can serve as a theoretical interpretation for the method. Expand
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. Expand
Contrastive Training for Improved Out-of-Distribution Detection
TLDR
This paper proposes and investigates the use of contrastive training to boost OOD detection performance, and introduces and employs the Confusion Log Probability (CLP) score, which quantifies the difficulty of the Ood detection task by capturing the similarity of inlier and outlier datasets. Expand
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
TLDR
PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets and is a good candidate for many industrial applications. Expand
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
TLDR
AnoGAN, a deep convolutional generative adversarial network is proposed to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Expand
Data-Efficient Image Recognition with Contrastive Predictive Coding
TLDR
This work revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations which make the variability in natural signals more predictable, and produces features which support state-of-the-art linear classification accuracy on the ImageNet dataset. Expand
Anomaly Detection with Robust Deep Autoencoders
TLDR
Novel extensions to deep autoencoders are demonstrated which not only maintain a deep autenkocoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. Expand
Unsupervised Feature Learning via Non-parametric Instance Discrimination
TLDR
This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Expand
Towards Visually Explaining Variational Autoencoders
  • WenQian Liu, Runze Li, +5 authors O. Camps
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
TLDR
This work proposes the first technique to visually explain VAEs by means of gradient-based attention, and presents methods to generate visual attention from the learned latent space, and shows how such attention explanations serve more than just explaining VAE predictions. Expand
...
1
2
3
...