Keyword [Resolution Discrepancy]

Touvron H, Vedaldi A, Douze M, et al. Fixing the train-test resolution discrepancy[J]. arXiv preprint arXiv:1906.06423, 2019.

Keyword [ImageNet-C] [ImageNet-P] [Image Corruption]

Geirhos R, Rubisch P, Michaelis C, et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness[C]. international conference on learning representations, 2019.

Keyword [Stochastic Depth]

Huang G, Sun Y, Liu Z, et al. Deep networks with stochastic depth[C]//European conference on computer vision. Springer, Cham, 2016: 646-661.

Keyword [EfficientNet]

Tan M, Le Q V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks[C]. international conference on machine learning, 2019: 6105-6114.

Liu C, Chen L C, Schroff F, et al. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 82-92.

Keyword [Stochastic Depth] [EfficientNet]

Xie Q, Hovy E, Luong M T, et al. Self-training with Noisy Student improves ImageNet classification[J]. arXiv preprint arXiv:1911.04252, 2019.

Keyword [DeepLabv3+] [Encoder-Decoder] [Xception]

Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 801-818.

Keyword [DeepLabv3] [Dilated Conv] [ASPP]

Chen L, Papandreou G, Schroff F, et al. Rethinking Atrous Convolution for Semantic Image Segmentation[J]. arXiv: Computer Vision and Pattern Recognition, 2017.

Keyword [DeepLabv2] [Dilated Conv] [ASPP] [CRF]

Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(4): 834-848.

Keyword [DeepLabv1] [Dilated Conv] [CRF]

Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv:1412.7062, 2014.