Keyword [GSNN] [GGNN]

Marino K, Salakhutdinov R, Gupta A. The more you know: Using knowledge graphs for image classification[J]. arXiv preprint arXiv:1612.04844, 2016.

Battaglia P W, Hamrick J B, Bapst V, et al. Relational inductive biases, deep learning, and graph networks[J]. arXiv preprint arXiv:1806.01261, 2018.

Keyword [Semantic Embedding] [Transfer Knowledge] [GCN]

Wang X, Ye Y, Gupta A. Zero-shot recognition via semantic embeddings and knowledge graphs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6857-6866.

Samangouei P, Kabkab M, Chellappa R. Defense-gan: Protecting classifiers against adversarial attacks using generative models[J]. arXiv preprint arXiv:1805.06605, 2018.

Buckman J, Roy A, Raffel C, et al. Thermometer encoding: One hot way to resist adversarial examples[J]. 2018.

Song Y, Kim T, Nowozin S, et al. Pixeldefend: Leveraging generative models to understand and defend against adversarial examples[J]. arXiv preprint arXiv:1710.10766, 2017.

Athalye A, Carlini N, Wagner D. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples[J]. arXiv preprint arXiv:1802.00420, 2018.

Keyword [Deep Image Prior]

Ilyas A, Jalal A, Asteri E, et al. The robust manifold defense: Adversarial training using generative models[J]. arXiv preprint arXiv:1712.09196, 2017.

Keyword [Universal Adversarial Perturbations]

Khrulkov V, Oseledets I. Art of singular vectors and universal adversarial perturbations[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8562-8570.

Keyword [Universal Adversarial Perturbations]

Poursaeed O, Katsman I, Gao B, et al. Generative adversarial perturbations[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 4422-4431.