Generating Natural Adversarial Remote Sensing Images
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Nicolas Courty | Rémi Flamary | Kilian Fatras | Jean-Christophe Burnel | N. Courty | Rémi Flamary | Kilian Fatras | Jean-Christophe Burnel
[1] Gabriel Peyré,et al. Learning Generative Models with Sinkhorn Divergences , 2017, AISTATS.
[2] Olac Fuentes,et al. On the Defense Against Adversarial Examples Beyond the Visible Spectrum , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).
[3] Xiao Xiang Zhu,et al. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.
[4] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[5] Nicolas Audebert,et al. Deep Learning for Classification of Hyperspectral Data: A Comparative Review , 2019, IEEE Geoscience and Remote Sensing Magazine.
[6] Olac Fuentes,et al. Integrated Learning and Feature Selection for Deep Neural Networks in Multispectral Images , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[7] Youngjoo Jo,et al. SC-FEGAN: Face Editing Generative Adversarial Network With User’s Sketch and Color , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[8] Ryan R. Curtin,et al. Detecting Adversarial Samples from Artifacts , 2017, ArXiv.
[9] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[10] Ting-Chun Wang,et al. Image Inpainting for Irregular Holes Using Partial Convolutions , 2018, ECCV.
[11] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[12] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[13] Rui Shu. AC-GAN Learns a Biased Distribution , 2017 .
[14] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[15] Taesung Park,et al. Semantic Image Synthesis With Spatially-Adaptive Normalization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Asja Fischer,et al. On the regularization of Wasserstein GANs , 2017, ICLR.
[17] Sameer Singh,et al. Generating Natural Adversarial Examples , 2017, ICLR.
[18] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[19] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[20] R. Gribonval,et al. Learning with minibatch Wasserstein : asymptotic and gradient properties , 2019, AISTATS.
[21] Jun Zhu,et al. Towards Robust Detection of Adversarial Examples , 2017, NeurIPS.
[22] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Gabriel Peyré,et al. Computational Optimal Transport , 2018, Found. Trends Mach. Learn..
[24] Nicholas Carlini,et al. Unrestricted Adversarial Examples , 2018, ArXiv.
[25] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Nicolas Courty,et al. A Cycle Gan Approach for Heterogeneous Domain Adaptation in Land Use Classification , 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.
[27] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[28] Yang Wang,et al. MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.
[29] Han Zhang,et al. Improving GANs Using Optimal Transport , 2018, ICLR.
[30] Junjun Jiang,et al. Edge-Enhanced GAN for Remote Sensing Image Superresolution , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[31] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[32] Wojciech Czaja,et al. Adversarial examples in remote sensing , 2018, SIGSPATIAL/GIS.
[33] Yang Song,et al. Constructing Unrestricted Adversarial Examples with Generative Models , 2018, NeurIPS.
[34] Fan Zhang,et al. Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.
[35] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[36] Haifeng Li,et al. Adversarial Example in Remote Sensing Image Recognition , 2019, ArXiv.
[37] 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).
[38] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[39] Bertrand Le Saux,et al. Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.
[40] Jianhua Lu,et al. GAN-NL: Unsupervised Representation Learning for Remote Sensing Image Classification , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).