异常检测最新论文列表
2024-07-04 12:03:07
Anomaly detection paper list
- github
Survey Paper
- Deep Learning for Anomaly Detection: A Survey | [arXiv’ 19] |
[pdf] - Anomalous Instance Detection in Deep Learning: A Survey | [arXiv’ 20] |
[pdf] - Deep Learning for Anomaly Detection: A Review | [arXiv’ 20] |
[pdf]
Table of Contents
- Time-series anomaly detection
- Video anomaly detection
- Image anomaly detection
- Anomaly Classification target
- Out-Of-Distribution(OOD) Detection target
- Anomaly Segmentation target
Time-series anomaly detection (need to survey more…)
- Anomaly Detection of Time Series | [Thesis’ 10] |
[pdf] - Long short term memory networks for anomaly detection in time series | [ESANN’ 15] |
[pdf] - LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems | [arXiv’ 16] |
[pdf] - Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data | [arXiv’ 17] |
[pdf] - Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis | [ICMLA’ 17] |
[pdf] - Truth Will Out: Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems | [ACM CCS '18] |
[pdf] - Time-Series Anomaly Detection Service at Microsoft | [KDD’ 19] |
[pdf] - Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network | [KDD’ 19] |
[pdf] - A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series | Under Review |
[code] - BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time | [IJCAI 19] |
[pdf] - MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams | [AAAI’ 20] |
[pdf]|[code] - Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network | [NeurIPS’ 20]
- Anomaly Detection of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder | [TNNLS’ 20]
Video-level anomaly detection
- Abnormal Event Detection in Videos using Spatiotemporal Autoencoder | [ISNN’ 17] |
[pdf] - Real-world Anomaly Detection in Surveillance Videos | [arXiv’ 18] |
[pdf][project page] - Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling | [CVPR Workshop’ 18] |
[pdf] - Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection | [CVPR Workshop’ 18] |
[pdf] - Detecting Abnormality without Knowing Normality: A Two-stage Approach for Unsupervised Video Abnormal Event Detection | [ACMMM’ 18] |
[link] - Motion-Aware Feature for Improved Video Anomaly Detection | [BMVC’ 19] |
[pdf] - Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos | [CVPRW’ 19] |
[pdf] - Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos | [CVPR’ 19] |
[pdf] - Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection | [CVPR’19] |
[pdf] - Graph Embedded Pose Clustering for Anomaly Detection | [CVPR’ 20] |
[pdf] - Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection | [CVPR’ 20] |
[pdf] - Learning Memory-Guided Normality for Anomaly Detection | [CVPR’ 20] |
[pdf] - Clustering-driven Deep Autoencoder for Video Anomaly Detection | [ECCV’ 20] |
[pdf] - CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection | [ECCV’ 20] |
[pdf] - Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events | [ACM MM’ 20] |
[pdf]|[code] - A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels | [IEEE SPL’ 20] |
[pdf] - Few-Shot Scene-Adaptive Anomaly Detection | [ECCV’ 20]
Image-level anomaly detection
One Class (Anomaly) Classification target
- Estimating the Support of a High- Dimensional Distribution [OC-SVM] | [Journal of Neural Computation’ 01] |
[pdf] - A Survey of Recent Trends in One Class Classification | [AICS’ 09] |
[pdf] - Anomaly detection using autoencoders with nonlinear dimensionality reduction | [MLSDA Workshop’ 14] |
[link] - A review of novelty detection | [Signal Processing’ 14] |
[link] - Variational Autoencoder based Anomaly Detection using Reconstruction Probability | [SNU DMC Tech’ 15] |
[pdf] - High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning | [Pattern Recognition’ 16] |
[link] - Transfer Representation-Learning for Anomaly Detection | [ICML’ 16] |
[pdf] - Outlier Detection with Autoencoder Ensembles | [SDM’ 17] |
[pdf] - Provable self-representation based outlier detection in a union of subspaces | [CVPR’ 17] |
[pdf] - [ALOCC]Adversarially Learned One-Class Classifier for Novelty Detection | [CVPR’ 18] |
[pdf][code] - Learning Deep Features for One-Class Classification | [arXiv’ 18] |
[pdf][code] - Efficient GAN-Based Anomaly Detection | [arXiv’ 18] |
[pdf] - Hierarchical Novelty Detection for Visual Object Recognition | [CVPR’ 18] |
[pdf] - Deep One-Class Classification | [ICML’ 18] |
[pdf] - Reliably Decoding Autoencoders’ Latent Spaces for One-Class Learning Image Inspection Scenarios | [OAGM Workshop’ 18] |
[pdf] - q-Space Novelty Detection with Variational Autoencoders | [arXiv’ 18] |
[pdf] - GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | [ACCV’ 18] |
[pdf] - Deep Anomaly Detection Using Geometric Transformations | [NIPS’ 18] |
[pdf] - Generative Probabilistic Novelty Detection with Adversarial Autoencoders | [NIPS’ 18] |
[pdf][code] - A loss framework for calibrated anomaly detection | [NIPS’ 18] |
[pdf] - A Practical Algorithm for Distributed Clustering and Outlier Detection | [NIPS’ 18] |
[pdf] - Efficient Anomaly Detection via Matrix Sketching | [NIPS’ 18] |
[pdf] - Adversarially Learned Anomaly Detection | [IEEE ICDM’ 18] |
[pdf] - Anomaly Detection With Multiple-Hypotheses Predictions | [ICML’ 19] |
[pdf] - Exploring Deep Anomaly Detection Methods Based on Capsule Net | [ICMLW’ 19] |
[pdf] - Latent Space Autoregression for Novelty Detection | [CVPR’ 19] |
[pdf] - OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations | [CVPR’ 19] |
[pdf] - Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training | [arXiv’ 19] |
[pdf] - Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty | [NeurIPS’ 19] |
[pdf][code] - Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network | [NeurIPS’ 19] |
[pdf][code] - Classification-Based Anomaly Detection for General Data | [ICLR’ 20] |
[pdf] - Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | [ICLR’ 20] |
[pdf] - RaPP: Novelty Detection with Reconstruction along Projection Pathway | [ICLR’ 20] |
[pdf] - Novelty Detection Via Blurring | [ICLR’ 20] |
[pdf] - Deep Semi-Supervised Anomaly Detection | [ICLR’ 20] |
[pdf] - Robust anomaly detection and backdoor attack detection via differential privacy | [ICLR’ 20] |
[pdf] - Classification-Based Anomaly Detection for General Data | [ICLR’ 20] |
[pdf] - Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm | [CVPR’ 20] |
[pdf] - Deep End-to-End One-Class Classifier | [IEEE TNNLS’ 20] |
[pdf] - Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection | [ECCV’ 20] |
[pdf] - Backpropagated Gradient Representations for Anomaly Detection | [ECCV’ 20]
- CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | [NeurIPS’ 20] |
[pdf]|[code] - Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework | [arXiv’ 20] |
[pdf]
Out-of-Distribution(OOD) Detection target
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks | [ICLR’ 17] |
[pdf] - [ODIN] Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks | [ICLR’ 18] |
[pdf] - Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples | [ICLR’ 18] |
[pdf] - Learning Confidence for Out-of-Distribution Detection in Neural Networks | [arXiv’ 18] |
[pdf] - Out-of-Distribution Detection using Multiple Semantic Label Representations | [NIPS’ 18] |
[pdf] - A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks | [NIPS’ 18] |
[pdf] - Deep Anomaly Detection with Outlier Exposure | [ICLR’ 19] |
[pdf] - Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem | [CVPR’ 19] |
[pdf] - Outlier Exposure with Confidence Control for Out-of-Distribution Detection | [arXiv’ 19] |
[pdf][code] - Likelihood Ratios for Out-of-Distribution Detection | [NeurIPS’ 19] |
[pdf] - Outlier Detection in Contingency Tables Using Decomposable Graphical Models | [SJS’ 19] |
[pdf][code] - Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models | [ICLR’ 20] |
[pdf] - Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks | [ICML Workshop’ 20] |
[pdf] - Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data | [CVPR’ 20] |
[pdf] - A Boundary Based Out-Of-Distribution Classifier for Generalized Zero-Shot Learning | [ECCV’ 20] |
[pdf] - Provable Worst Case Guarantees for the Detection of Out-of-distribution Data | [NeurIPS’ 20] |
[pdf]|[code] - On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law | [NeurIPS’ 20] |
[pdf] - Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder | [NeurIPS’ 20] |
[pdf] - OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification | [NeurIPS’ 20]
- Energy-based Out-of-distribution Detection | [NeurIPS’ 20] |
[pdf] - Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples | [NeurIPS’ 20]
- Why Normalizing Flows Fail to Detect Out-of-Distribution Data | [NeurIPS’ 20] |
[pdf]|[code] - Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features | [NeurIPS’ 20] |
[pdf] - Further Analysis of Outlier Detection with Deep Generative Models | [NeurIPS’ 20]
- CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | [NeurIPS’ 20] |
[pdf]|[code]
Unsupervised Anomaly Segmentation target
- Anomaly Detection and Localization in Crowded Scenes | [TPAMI’ 14] |
[pdf] - Novelty detection in images by sparse representations | [IEEE Symposium on IES’ 14] |
[link] - Detecting anomalous structures by convolutional sparse models | [IJCNN’ 15] |
[pdf] - Real-Time Anomaly Detection and Localization in Crowded Scenes | [CVPR Workshop’ 15] |
[pdf] - Learning Deep Representations of Appearance and Motion for Anomalous Event Detection | [BMVC’ 15] |
[pdf] - Scale-invariant anomaly detection with multiscale group-sparse models | [IEEE ICIP’ 16] |
[link] - [AnoGAN] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery | [IPMI’ 17] |
[pdf] - Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes | [Journal of Computer Vision and Image Understanding’ 17] |
[pdf] - Anomaly Detection using a Convolutional Winner-Take-All Autoencoder | [BMVC’ 17] |
[pdf] - Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity | [Sensors’ 17] |
[pdf] - Defect Detection in SEM Images of Nanofibrous Materials | [IEEE Trans. on Industrial Informatics’ 17] |
[pdf] - Abnormal event detection in videos using generative adversarial nets | [ICIP’ 17] |
[link] - An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos | [arXiv’ 18] |
[pdf] - Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders | [arXiv’ 18] |
[pdf] - Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier | [IS&T EI’ 18] |
[pdf] - Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images | [arXiv’ 18] |
[pdf] - AVID: Adversarial Visual Irregularity Detection | [arXiv’ 18] |
[pdf] - MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection | [CVPR’ 19] |
[pdf] - Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT | [IEEE TMI’ 19] |
[pdf] - Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings | [CVPR’ 20] |
[pdf] - Attention Guided Anomaly Detection and Localization in Images | [ECCV’ 20] |
[pdf] - Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images | [ECCV’ 20]
- Sub-Image Anomaly Detection with Deep Pyramid Correspondences | [arxiv’ 20]
pdfcode - Patch SVDD, Patch-level SVDD for Anomaly Detection and Segmentation | [arxiv’ 20]
pdfcode - Unsupervised anomaly segmentation via deep feature reconstruction | [Neurocomputing’ 20]
pdfcode - Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation | [PR]
pdfcode
- Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation | [PR]
持续更新…
- github
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