methodology-1

One-class classifier

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

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Use these libraries to find one-class-classifier-3 models and implementations

Datasets

COVID-19-CT-CXR

Riseholme-2021

Simulated micro-Doppler Signatures

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Most implemented papers

Adversarially Learned One-Class Classifier for Novelty Detection

M. Sabokrou, Mohammad Khalooei, M. Fathy, Ehsan Adeli•Sat Feb 24 2018

The results on MNIST and Caltech-256 image datasets, along with the challenging UCSD Ped2 dataset for video anomaly detection illustrate that the proposed method learns the target class effectively and is superior to the baseline and state-of-the-art methods.

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Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks

Anjith George, S. Marcel•Tue Jul 21 2020

A new framework for PAD is proposed using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN) and a novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks.

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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

Kihyuk Sohn, Tomas Pfister, Jinsung Yoon, Chun-Liang Li•Wed Apr 07 2021

This work proposes a two-stage framework for building anomaly detectors using normal training data only, which first learns self-supervised deep representations and then builds a generative one-class classifier on learned representations.

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Near out-of-distribution detection for low-resolution radar micro-Doppler signatures

Martin Bauw, S. Velasco-Forero, J. Angulo, C. Adnet, O. Airiau•Wed May 11 2022

An OODD use case for radar targets detection extensible to other kinds of sensors and detection scenarios is put forward, and a comparison of deep and non-deep OodD methods on simulated low-resolution pulse radar micro-Doppler signatures is proposed.

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Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier

E. Delp, Paolo Bestagini, S. Tubaro, F. Zhu, S. Yarlagadda, D. Güera•Sat Jan 27 2018

An algorithm for satellite image forgery detection and localization is proposed that works under the assumption that no forged images are available for training and is validated against different kinds of satellite images containing forgeries of different size and shape.

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Automatic support vector data description

Reza Sadeghi, J. Hamidzadeh•Wed Aug 17 2016

This work proposes automatic support vector data description (ASVDD) based on both validation degree, which is originated from fuzzy rough set to discover data characteristic, and assigning effective values for tuning parameters by chaotic bat algorithm, and demonstrates superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and AUC.

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Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification

C. Gautam, R. Balaji, S. K, Aruna Tiwari, K. Ahuja•Sun May 20 2018

A Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally and the parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process.

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Feature learning for fault detection in high-dimensional condition monitoring signals

Gabriel Michau, Yang Hu, Thomas Palmé, Olga Fink•Thu Oct 11 2018

An integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training, demonstrating a better performance in cases where condition monitoring data contain several non-informative signals.

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Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Stanislav Pidhorskyi, Ranya Almohsen, D. Adjeroh, Gianfranco Doretto•Thu Jul 05 2018

This work makes the computation of the novelty probability feasible because it linearize the parameterized manifold capturing the underlying structure of the inlier distribution, and shows how the probability factorizes and can be computed with respect to local coordinates of the manifold tangent space.

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Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

K. Czarnecki, Vahdat Abdelzad, Rick Salay, Taylor Denouden, Sachin Vernekar, Buu Phan•Tue Oct 22 2019

This paper proposes a new OOD detection approach that can be easily applied to an existing classifier and does not need to have access to OOD samples, and achieves substantially better results over multiple metrics.

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