暂无分享,去创建一个
[1] L. Wolf,et al. Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample , 2020, NeurIPS.
[2] Michal Irani,et al. InGAN: Capturing and Retargeting the “DNA” of a Natural Image , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[3] Ling Chen,et al. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection , 2018, KDD.
[4] Huidong Jin,et al. Deep Weakly-supervised Anomaly Detection , 2019 .
[5] Yu Cheng,et al. Deep Structured Energy Based Models for Anomaly Detection , 2016, ICML.
[6] Dani Lischinski,et al. Non-stationary texture synthesis by adversarial expansion , 2018, ACM Trans. Graph..
[7] Alexander Binder,et al. Deep One-Class Classification , 2018, ICML.
[8] Matthew Turk,et al. CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[9] Li Fei-Fei. Knowledge transfer in learning to recognize visual objects classes , 2006 .
[10] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[11] Harsha Vardhan Simhadri,et al. DROCC: Deep Robust One-Class Classification , 2020, ICML.
[12] Song Han,et al. Differentiable Augmentation for Data-Efficient GAN Training , 2020, NeurIPS.
[13] Chuan Li,et al. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.
[14] Yue Wang,et al. Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? , 2020, ECCV.
[15] Yedid Hoshen,et al. Classification-Based Anomaly Detection for General Data , 2020, ICLR.
[16] Charu C. Aggarwal,et al. Outlier Analysis , 2013, Springer New York.
[17] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[18] Longbing Cao,et al. Deep Reinforcement Learning for Unknown Anomaly Detection , 2020, ArXiv.
[19] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[20] Anton van den Hengel,et al. Deep Anomaly Detection with Deviation Networks , 2019, KDD.
[21] M. M. Moya,et al. One-class classifier networks for target recognition applications , 1993 .
[22] Sungroh Yoon,et al. Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation , 2020, ArXiv.
[23] Sungzoon Cho,et al. Variational Autoencoder based Anomaly Detection using Reconstruction Probability , 2015 .
[24] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Carsten Steger,et al. MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[27] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[28] Volker Tresp,et al. Few-Shot One-Class Classification via Meta-Learning , 2019, AAAI.
[29] James T. Kwok,et al. Generalizing from a Few Examples , 2019, ACM Comput. Surv..
[30] Michael Isard,et al. Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[31] Tero Karras,et al. Training Generative Adversarial Networks with Limited Data , 2020, NeurIPS.
[32] Anna Kruspe,et al. One-Way Prototypical Networks , 2019, ArXiv.
[33] Thomas G. Dietterich,et al. A Unifying Review of Deep and Shallow Anomaly Detection , 2020, Proceedings of the IEEE.
[34] Bernhard Schölkopf,et al. Support Vector Method for Novelty Detection , 1999, NIPS.
[35] Ran El-Yaniv,et al. Deep Anomaly Detection Using Geometric Transformations , 2018, NeurIPS.
[36] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[37] Yu-Chiang Frank Wang,et al. A Closer Look at Few-shot Classification , 2019, ICLR.
[38] Tali Dekel,et al. SinGAN: Learning a Generative Model From a Single Natural Image , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[39] Bodo Rosenhahn,et al. Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[40] Paul W. Fieguth,et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.
[41] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[42] Yee Whye Teh,et al. Do Deep Generative Models Know What They Don't Know? , 2018, ICLR.