异常检测最新论文列表

Anomaly detection paper list

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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

持续更新…

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