Deep One-Class Classification

By Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft. 2018

2,692 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Wojciech Samek
3. Chunhua Shen
4. Dacheng Tao
5. Kaiyang Zhou
6. Jingkang Yang
7. Ziwei Liu
8. Bo Li

Citations in the corpus

• Detection and Continual Learning of Novel Face Presentation Attacks (2021, methodology): “Our idea is based on enabling the network to identify new attack types as testing samples that are outside the training distribution samples (OTDS) in an embedding space [11, 29, 40, 19, 4].”
• Continual Learning by Using Information of Each Class Holistically (2021, background): “Since PCL learns one class at a time, it is related to one-class learning (Sch¨ olkopf et al. 2001; Tax and Duin 2004; Hu et al. 2020; Perera, Nallapati, and Xiang 2019; Ruff et al. 2018).”
• Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty (2019, background): “Deep SVDD [Ruff et al., 2018] uses convolutional networks to extract features from the raw pixels all while modelling one class, like OC-SVMs.”
• Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “3: Classification-based Methods [252], [253], [269], [289], [290], [291], [292]”
• Conditional Gaussian Distribution Learning for Open Set Recognition (2020, methodology): “In recent years, deep neural networks have also been used in anomaly detection, mainly based on autoencoders trained in an unsupervised manner [43, 2, 28, 44].”
• OpenOOD: Benchmarking Generalized Out-of-Distribution Detection (2022, methodology): “- Anomaly Detection ^ DeepSVDD [56] (ICML’18) 90.”
• Student-Teacher Feature Pyramid Matching for Anomaly Detection (2021, background): “Such deep features prove very effective in capturing the intrinsic characteristics of the normal data manifold [3, 10, 24, 32, 47].”
• HRN: A Holistic Approach to One Class Learning (2020, methodology): “The recent DSVDD (Deep Support Vector Data Description) proposed a deep learning solution to implement SVDD [68].”
• Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives (2021, background): “Accordingly, OCL is a challenging classification problem that is harder to solve than conventional classification problems, which try to discriminate between data from two or more categories using training consumption data that pertain to all the groups [39].”
• PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (2020, methodology): “3) compared to the state-of-the-art: One-class SVM [25], DeepSVDD [23], Multi-Head RotNet [15].”
• DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection (2021, background): “These are based on one-class methods [22, 7] which attempt to estimate a decision boundary around anomaly-free data that separates it from anomalous samples by assuming a unimodal distribution of the anomaly-free data.”
• Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019, methodology): “A natural choice for handling the problem is thus the oneclass classification methods, such as one-class SVM [5, 34] and deep one-class networks [31, 3], which seeks to learn a discriminative hyperplane surrounding the normal samples.”
• Learning Deep Features for One-Class Classification (2018, background): “Despite its importance, contemporary one-class classification schemes trained solely on the given concept have failed to produce promising results in real-world datasets [1], [31], [40] has achieved an Area Under the Curve in the range of 60%–65% for CIFAR10 dataset [21].”
• Deep Learning for Time Series Anomaly Detection: A Survey (2022, unknown): “Bayesian networks help refine observations due to their adaptability and ease in calculating probabilities.”
• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “[32], CL-MPPCA [67]; the density-estimation models: LOF [10], MPPCACD [76], DAGMM [92]; the clustering-based methods: DeepSVDD [60], THOC [64], ITAD [65]; the classic methods: OCSVM [68], OCSVM-based subsequence clustering (OCSVM*), IForest [46], IForest-based subsequence clustering (IForest*), Grad”
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “However, for unsupervised problems, such as anomaly detection or clustering, although deep models have also reached successes [27], [155], [156], shallow models (e.”
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “, [15], [16], [17], [18], [19], [20]; we provide a more detailed overview in Section 2).”