Detecting the Unexpected via Image Resynthesis
暂无分享,去创建一个
Pascal Fua | Mathieu Salzmann | Krzysztof Lis | Krishna K. Nakka | P. Fua | M. Salzmann | Krzysztof Lis | K. K. Nakka
[1] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[2] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[3] Ruigang Yang,et al. The ApolloScape Dataset for Autonomous Driving , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[4] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[5] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[6] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[8] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[9] Facebook,et al. Houdini : Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples , 2017 .
[10] Yann LeCun,et al. Transforming Neural-Net Output Levels to Probability Distributions , 1990, NIPS.
[11] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[12] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[13] Mingyan Liu,et al. Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation , 2018, ECCV.
[14] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[15] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[17] Shuichi Arai,et al. Inference with model uncertainty on indoor scene for semantic segmentation , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
[18] Eugenio Culurciello,et al. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation , 2016, ArXiv.
[19] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[20] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[21] Asim Munawar,et al. Real-time small obstacle detection on highways using compressive RBM road reconstruction , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).
[22] Andrew Blake,et al. "GrabCut" , 2004, ACM Trans. Graph..
[23] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[24] Alan L. Yuille,et al. Adversarial Examples for Semantic Segmentation and Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[25] Nicu Sebe,et al. Abnormal event detection in videos using generative adversarial nets , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[26] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Carsten Rother,et al. Deep Object Co-Segmentation , 2018, ACCV.
[28] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[29] B. Ravi Kiran,et al. An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos , 2018, J. Imaging.
[30] Shuichi Arai,et al. Deep convolutional encoder-decoder network with model uncertainty for semantic segmentation , 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).
[31] Roberto Cipolla,et al. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.
[32] Jan Kautz,et al. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Giovanni De Magistris,et al. Limiting the reconstruction capability of generative neural network using negative learning , 2017, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).
[34] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[35] Asim Munawar,et al. Structural inpainting of road patches for anomaly detection , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).
[36] Sebastian Ramos,et al. Lost and Found: detecting small road hazards for self-driving vehicles , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[37] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[38] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Shuichi Arai,et al. A Semantic Segmentation Method using Model Uncertainty , 2017 .
[40] Stefan Roth,et al. Lightweight Probabilistic Deep Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[42] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .
[43] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[44] Toby P. Breckon,et al. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , 2018, ACCV.
[45] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[46] Gang Yu,et al. Learning a Discriminative Feature Network for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.