Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers

Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. pp. 779–788 (2016)

2016

Cited alongside, same era.

Vasiljevic, I., Chakrabarti, A., Shakhnarovich, G.: Examining the impact of blur on recognition by convolutional networks. arXiv:1611.05760 [cs.CV] abs/1611.05760

Original

2016

Cited alongside, same era.

Zheng, S., Song, Y., Leung, T., Goodfellow, I.J.: Improving the robustness of deep neural networks via stability training. In: CVPR. pp. 4480–4488 (2016)

2016

Cited alongside, same era.

Carlini, N., Wagner, D.: Adversarial examples are not easily detected: Bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. pp. 3–14. AISec ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3128572.3140444, http://doi.acm.org/10.1145/3128572.3140444

2017

Cited alongside, same era.

Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. 2017 IEEE Symposium on Security and Privacy (SP) (2017)

2017

Cited alongside, same era.

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. In: TPAMI (2017), http://arxiv.org/abs/1606.00915

Original

2017

Cited alongside, same era.

Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017), http://arxiv.org/abs/1706.05587

Original

2017

Cited alongside, same era.

Ruderman, A., Rabinowitz, N.C., Morcos, A.S., Zoran, D.: Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs. arXiv preprint arXiv:1804.04438 (2018)

Original

2018

Later among the works it cites.

Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. IJCV 126

2018

Later among the works it cites.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR (2018)

2018

Later among the works it cites.

Azulay, A., Weiss, Y.: Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning Research 20

2019

Later among the works it cites.

Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

2019

Later among the works it cites.

Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., Madry, A.: Exploring the landscape of spatial robustness. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 1802–1811. PMLR, Long Beach, California, USA (Jun 2019), http://proceedings.mlr.press/v97/engstrom19a.html

2019

Later among the works it cites.

Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In: ICLR (May 2019), https://openreview.net/forum?id=Bygh9j09KX

2019

Later among the works it cites.

Gilmer, J., Ford, N., Carlini, N., Cubuk, E.: Adversarial examples are a natural consequence of test error in noise. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 2280–2289. PMLR, Long Beach, California, USA (Jun 2019), http://proceedings.mlr.press/v97/gilmer19a.html

2019

Later among the works it cites.

Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. ICLR (2019)

2019

Later among the works it cites.

Laermann, J., Samek, W., Strodthoff, N.: Achieving generalizable robustness of deep neural networks by stability training. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) Pattern Recognition. pp. 360–373. Springer International Publishing, Cham (2019)

2019

Later among the works it cites.

Lopes, R.G., Yin, D., Poole, B., Gilmer, J., Cubuk, E.D.: Improving robustness without sacrificing accuracy with patch gaussian augmentation. arXiv preprint arXiv:1906.02611 (2019)

Original

2019

Later among the works it cites.

Sakaridis, C., Dai, D., Van Gool, L.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: ICCV (2019)

2019

Later among the works it cites.

Takahashi, R., Matsubara, T., Uehara, K.: Data augmentation using random image cropping and patching for deep CNNs. IEEE Transactions on Circuits and Systems for Video Technology (2019)

2019

Later among the works it cites.

Volk, G., Stefan, M., von Bernuth, A., Hospach, D., Bringmann, O.: Towards robust cnn-based object detection through augmentation with synthetic rain variations. In: ITSC (2019)

2019

Later among the works it cites.

Xie, Q., Hovy, E., Luong, M.T., Le, Q.V.: Self-training with noisy student improves imagenet classification. arXiv preprint arXiv:1911.04252 (2019)

Original

2019

Later among the works it cites.

Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: ICCV. pp. 6023–6032 (2019)

2019

Later among the works it cites.

Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: A simple data processing method to improve robustness and uncertainty. ICLR (2020)

2020

Closest in time.

Kamann, C., Rother, C.: Benchmarking the robustness of semantic segmentation models. In: CVPR (June 2020)

2020

Closest in time.

Kamann, C., Rother, C.: Benchmarking the robustness of semantic segmentation models with respect to common corruptions. International Journal of Computer Vision pp. 1–22 (2020)

2020

Closest in time.