Contrastive Predictive Coding for Anomaly Detection
- URL: http://arxiv.org/abs/2107.07820v1
- Date: Fri, 16 Jul 2021 11:04:35 GMT
- Title: Contrastive Predictive Coding for Anomaly Detection
- Authors: Puck de Haan, Sindy L\"owe
- Abstract summary: Contrastive Predictive Coding model (arXiv:1807.03748) used for anomaly detection and segmentation.
We show that its patch-wise contrastive loss can directly be interpreted as an anomaly score.
Model achieves promising results for both anomaly detection and segmentation on the MVTec-AD dataset.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 losses directly for both anomaly
detection and segmentation. In this paper, we close this gap by making use of
the Contrastive Predictive Coding model (arXiv:1807.03748). We show that its
patch-wise contrastive loss can directly be interpreted as an anomaly score,
and how this allows for the creation of anomaly segmentation masks. The
resulting model achieves promising results for both anomaly detection and
segmentation on the challenging MVTec-AD dataset.
Related papers
- Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination [20.4008901760593]
We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end.
Our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination.
arXiv Detail & Related papers (2024-11-14T16:10:15Z) - Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD.
It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
arXiv Detail & Related papers (2024-10-14T08:41:31Z) - MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring [2.394081903745099]
We propose MeLIAD, a novel methodology for interpretable anomaly detection.
MeLIAD is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies.
Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance.
arXiv Detail & Related papers (2024-09-20T16:01:43Z) - Anomaly Detection by Context Contrasting [57.695202846009714]
Anomaly detection focuses on identifying samples that deviate from the norm.
Recent advances in self-supervised learning have shown great promise in this regard.
We propose Con$$, which learns through context augmentations.
arXiv Detail & Related papers (2024-05-29T07:59:06Z) - AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
Anomaly inspection plays an important role in industrial manufacture.
Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data.
We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
arXiv Detail & Related papers (2023-12-10T05:13:40Z) - ReConPatch : Contrastive Patch Representation Learning for Industrial
Anomaly Detection [5.998761048990598]
We introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model.
Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset.
arXiv Detail & Related papers (2023-05-26T07:59:36Z) - Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold.
We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples.
We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
arXiv Detail & Related papers (2022-06-23T14:16:30Z) - SLA$^2$P: Self-supervised Anomaly Detection with Adversarial
Perturbation [77.71161225100927]
Anomaly detection is a fundamental yet challenging problem in machine learning.
We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
arXiv Detail & Related papers (2021-11-25T03:53:43Z) - Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
We introduce a novel weakly-supervised anomaly detection framework to train detection models.
The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability.
Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
arXiv Detail & Related papers (2021-08-01T14:33:17Z) - Constrained Contrastive Distribution Learning for Unsupervised Anomaly
Detection and Localisation in Medical Images [23.79184121052212]
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images.
We propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD)
Our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets.
arXiv Detail & Related papers (2021-03-05T01:56:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.