NM-GAN: Noise-modulated generative adversarial network for video anomaly detection
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Tong Jia | Xingya Chang | Ming Xu | Dongyue Chen | Lingyi Yue | Dongyue Chen | Tong Jia | Xingya Chang | Ming Xu | Lingyi Yue
[1] Hichem Snoussi,et al. G2D: Generate to Detect Anomaly , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[2] Xuelong Li,et al. Collective Representation for Abnormal Event Detection , 2017, Journal of Computer Science and Technology.
[3] Ramesh Nallapati,et al. OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Toby P. Breckon,et al. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , 2018, ACCV.
[5] Yong Man Ro,et al. Stan: Spatio- Temporal Adversarial Networks for Abnormal Event Detection , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[6] Chunsheng Liu,et al. Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes , 2021, IEEE Transactions on Multimedia.
[7] Anoop Cherian,et al. GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[8] Simone Calderara,et al. Latent Space Autoregression for Novelty Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Hong Liu,et al. Online growing neural gas for anomaly detection in changing surveillance scenes , 2017, Pattern Recognit..
[10] Nicu Sebe,et al. Abnormal event detection in videos using generative adversarial nets , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[11] Jean Meunier,et al. Anomaly Detection in Video Sequence With Appearance-Motion Correspondence , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Chen Shen,et al. Spatio-Temporal AutoEncoder for Video Anomaly Detection , 2017, ACM Multimedia.
[13] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[14] Shenghua Gao,et al. A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[15] Chengdong Wu,et al. An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders , 2019 .
[16] Sanjay Chawla,et al. Robust, Deep and Inductive Anomaly Detection , 2017, ECML/PKDD.
[17] Shenghua Gao,et al. Future Frame Prediction for Anomaly Detection - A New Baseline , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Jonghyun Choi,et al. Learning Temporal Regularity in Video Sequences , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Xiushan Nie,et al. Dual Discriminator Generative Adversarial Network for Video Anomaly Detection , 2020, IEEE Access.
[20] Mahmood Fathy,et al. AVID: Adversarial Visual Irregularity Detection , 2018, ACCV.
[21] Mahmood Fathy,et al. Deep End-to-End One-Class Classifier , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[22] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[23] Yong Haur Tay,et al. Abnormal Event Detection in Videos using Spatiotemporal Autoencoder , 2017, ISNN.
[24] Radu Tudor Ionescu,et al. Unmasking the Abnormal Events in Video , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[25] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[26] Heitor Silvério Lopes,et al. A study of deep convolutional auto-encoders for anomaly detection in videos , 2018, Pattern Recognit. Lett..
[27] Bumsub Ham,et al. Learning Memory-Guided Normality for Anomaly Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Shenghua Gao,et al. Remembering history with convolutional LSTM for anomaly detection , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).
[29] Mahmood Fathy,et al. Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes , 2016, Comput. Vis. Image Underst..
[30] Mahmood Fathy,et al. Adversarially Learned One-Class Classifier for Novelty Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Dong Li,et al. A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples , 2017, Pattern Recognit..
[32] H. Snoussi,et al. G2D: Generate to Detect Anomalies , 2020 .
[33] B. Ravi Kiran,et al. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos , 2018, J. Imaging.
[34] Nicu Sebe,et al. Detecting anomalous events in videos by learning deep representations of appearance and motion , 2017, Comput. Vis. Image Underst..
[35] Svetha Venkatesh,et al. Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[36] Nicu Sebe,et al. Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds , 2017, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[37] Mei Chen,et al. Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos , 2020, IEEE Transactions on Multimedia.
[38] Guojun Wen,et al. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model , 2018, Sensors.