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Self-organization in a perceptual network
Linsker, R · 1988
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
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Outliers detection in multivariate time series by independent component analysis
Baragona, R. and Battaglia, F · 2007
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Isolation forest
Liu, F. T., Ting, K. M., and Zhou, Z.-H · 2008
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, M. U. and Hyvärinen, A · 2012
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Learning word embeddings efficiently with noise-contrastive estimation
Mnih, A. and Kavukcuoglu, K · 2013
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., and Brox, T · 2015
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Large-scale unusual time series detection
Hyndman, R. J., Wang, E., and Laptev, N · 2015
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High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Erfani, S. M., Rajasegarar, S., Karunasekera, S., and Leckie, C · 2016
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Lstm-based encoder-decoder for multi-sensor anomaly detection
Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., and Shroff, G · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Misra, I., Zitnick, C. L., and Hebert, M · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Acoustic novelty detection with adversarial autoencoders
Principi, E., Vesperini, F., Squartini, S., and Piazza, F · 2017
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Learning to compose domain-specific transformations for data augmentation
Ratner, A. J., Ehrenberg, H. R., Hussain, Z., Dunnmon, J., and Ré, C · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G · 2017
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A bayesian data augmentation approach for learning deep models
Tran, T., Pham, T., Carneiro, G., Palmer, L., and Reid, I · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Zhang, R., Isola, P., and Efros, A. A · 2017
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Anomaly detection with robust deep autoencoders
Zhou, C. and Paffenroth, R. C · 2017
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Ganomaly: Semi-supervised anomaly detection via adversarial training
Akcay, S., Atapour-Abarghouei, A., and Breckon, T. P · 2018
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The uea multivariate time series classification archive, 2018
Bagnall, A., Dau, H. A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., and Keogh, E · 2018
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Unsupervised detection of lesions in brain mri using constrained adversarial auto-encoders
Chen, X. and Konukoglu, E · 2018
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Image anomaly detection with generative adversarial networks
Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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Population based augmentation: Efficient learning of augmentation policy schedules
Ho, D., Liang, E., Chen, X., Stoica, I., and Abbeel, P · 2019
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Rapp: Novelty detection with reconstruction along projection pathway
Kim, K. H., Shim, S., Lim, Y., Jeon, J., Choi, J., Kim, B., and Yoon, A. S · 2019
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Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S · 2019
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Ocgan: One-class novelty detection using gans with constrained latent representations
Perera, P., Nallapati, R., and Xiang, B · 2019
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2019
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Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., and Kloft, M · 2018
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
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Deep anomaly detection using geometric transformations
Golan, I. and El-Yaniv, R · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2018
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Outlier detection for multidimensional time series using deep neural networks
Kieu, T., Yang, B., and Jensen, C. S · 2018
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Deepant: A deep learning approach for unsupervised anomaly detection in time series
Munir, M., Siddiqui, S. A., Dengel, A., and Ahmed, S · 2018
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Adversarial autoaugment
Zhang, X., Wang, Q., Zhang, J., and Zhong, Z · 2019
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Extreme classification via adversarial softmax approximation
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Classification-based anomaly detection for general data
Bergman, L. and Hoshen, Y · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Drocc: Deep robust one-class classification
Goyal, S., Raghunathan, A., Jain, M., Simhadri, H. V., and Jain, P · 2020
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Ruiz, A. P., Flynn, M., Large, J., Middlehurst, M., and Bagnall, A · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Shen, L., Li, Z., and Kwok, J · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., and Shin, J · 2020
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Learning perturbation sets for robust machine learning
Wong, E. and Kolter, J. Z · 2020
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A unifying review of deep and shallow anomaly detection
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Learning and evaluating representations for deep one-class classification
Sohn, K., Li, C.-L., Yoon, J., Jin, M., and Pfister, T · 2021
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Viewmaker networks: Learning views for unsupervised representation learning
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