Anomaly Detection异常检测技术用于各种领域,如入侵检测、欺诈检测、故障检测、系统健康监测、传感器网络事件检测和生态系统干扰检测等。它通常用于在预处理中删除从数据集的异常数据。在监督式学习中,去除异常数据的数据集往往会在统计上显著提升准确性。
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque
ICLR, (2020)
We show that input complexity has a strong effect in those likelihoods, and pose that it is the main culprit for the puzzling results of using generative models’ likelihoods for OOD detection
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Muhammad Zaigham Zaheer, Jin-Ha Lee,Marcella Astrid, Seung-Ik Lee
CVPR, pp.14171-14181, (2020)
This paper presents an adversarially learned approach in which both the generator and the discriminator are utilized to perform a stable and robust anomaly detection
Cited by3BibtexViews18Links
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CVPR, pp.10948-10957, (2020)
Our further analysis using a larger scale image dataset shows that the data with only semantic shift is harder to detect, pointing out a challenge for future works to address
Cited by3BibtexViews65Links
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VQA-CP has become the standard OOD benchmark for visual question answering, but we discovered three troubling practices in its current use
Cited by2BibtexViews38Links
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ICLR, (2020)
In this paper, inspired by the fact that differential privacy implies stability, we apply DP noise to improve the performance of outlier detection and novelty detection, with an extension to backdoor attack detection
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NIPS 2020, (2020)
The energy scores are provably aligned with the density of inputs, and as a result, yield substantially improved OOD detection performance
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NIPS 2020, (2020)
To provide insights into prior results, part of our discussion has focused on an in-depth exploration of the popular class of normalizing flows based on affine coupling layers
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CVPR, pp.12170-12179, (2020)
We have shown that framing video anomaly detection as a self-training deep ordinal regression task overcomes some of the key limitations of existing approaches to this important problem
Cited by1BibtexViews116Links
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NIPS 2020, (2020)
We propose Likelihood Regret, an OOD score for Variational Auto-encoders that is effective on all tasks we evaluated
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Siddharth Bhatia,Bryan Hooi, Minji Yoon,Kijung Shin,Christos Faloutsos
national conference on artificial intelligence, (2020)
Streaming Microcluster Detection: We propose a novel streaming approach for detecting microcluster anomalies, requiring constant time and memory
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Sungik Choi,Sae-Young Chung
ICLR, (2020)
6 CONCLUSION In this work, blurred images are introduced as adversarial examples in deep Out-of distribution detection
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Fei Ye,Huangjie Zheng, Chaoqin Huang,Ya Zhang
To optimize the objective function under the unsupervised setting, we investigate the condition to bypass the third term and get a lower bound on the objective function which can be considered as a trade-off between the mutual information and the entropy
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Muhammad Zaigham Zaheer, Arif Mahmood,Marcella Astrid,Seung-Ik Lee
european conference on computer vision, pp.358-376, (2020)
A normalcy suppression mechanism is proposed which collaborates with the backbone network in detecting anomalies by learning to suppress the features corresponding to the normal portions of an input video
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Lifeng Shen, Zhuocong Li,James Kwok
NIPS 2020, (2020)
Inspired by deep support vector data description, we propose in this paper the Temporal Hierarchical One-Class network
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NIPS 2020, (2020)
We have proposed a novel loss function for Dirichlet Prior Network models that maximizes the representation gap between in-domain and OOD examples
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NIPS 2020, (2020)
Our result is to be compared with the recent discovery that energy-based models assign lower likelihood to outliers under this setting, which naturally leads to the question of whether a calibrated deep generative models should always have a similar behavior
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Taewon Jeong,Heeyoung Kim
NIPS 2020, (2020)
We proposed OOD-model-agnostic meta learning, which is a meta-learning method used for implementing K-shot N -way classification and OOD detection simultaneously
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Guang Yu, Siqi Wang,Zhiping Cai,En Zhu, Chuanfu Xu,Jianping Yin, Marius Kloft
MM '20: The 28th ACM International Conference on Multimedia Seattle WA USA ..., pp.583-591, (2020)
We propose Video Event Completion as a new solution to deep neural network based Video anomaly detection
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Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, Seung-Ik Lee
IEEE Signal Processing Letters, (2020): 1705-1709
We propose an approach for anomalous event detection using such video-level labels
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european conference on computer vision, pp.572-588, (2020)
We present an Out-of-Distribution classifier for the Generalized Zero-Shot learning problem
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Keywords
Anomaly DetectionOutlier DetectionAutoencoderDeep LearningDeep Neural NetworksHigh-dimensional DataNovelty DetectionProbability DistributionProposed MethodSemi-supervised Learning
Authors
Jinwoo Shin
Paper 4
Paul Bergmann
Paper 3
Kimin Lee
Paper 3
Dan Hendrycks
Paper 3
Mahmood Fathy
Paper 3
Honglak Lee
Paper 3
Carsten Steger
Paper 3
Mohammad Sabokrou
Paper 3
Ehsan Adeli
Paper 2
Matthias A. Hein
Paper 2