Keyword [Adversarial Patch]

Brown T B, Mané D, Roy A, et al. Adversarial patch[J]. arXiv preprint arXiv:1712.09665, 2017.

Dosovitskiy A, Brox T. Inverting Visual Representations with Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. 2016.

Mahendran A, Vedaldi A. Understanding deep image representations by inverting them[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5188-5196.

Shin Y S , Cho Y , Pandey G , et al. [IEEE OCEANS 2016 MTS/IEEE Monterey - Monterey, CA, USA (2016.9.19-2016.9.23)] OCEANS 2016 MTS/IEEE Monterey - Estimation of ambient light and transmission map with common convolutional architecture[J]. 2016:1-7.

Tai Y , Yang J , Liu X . Image Super-Resolution via Deep Recursive Residual Network[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2017.

Keyword [PixelCNN] [PixelRNN]

Oord A, Kalchbrenner N, Kavukcuoglu K. Pixel recurrent neural networks[J]. arXiv preprint arXiv:1601.06759, 2016.

Li X, Wu J, Lin Z, et al. Recurrent squeeze-and-excitation context aggregation net for single image deraining[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 254-269.

Liu D, Wen B, Fan Y, et al. Non-local recurrent network for image restoration[C]//Advances in Neural Information Processing Systems. 2018: 1673-1682.

Liu W, Luo W, Lian D, et al. Future frame prediction for anomaly detection–a new baseline[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6536-6545.

Akcay S, Atapour-Abarghouei A, Breckon T P. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training[J]. 2018.