Rupprecht C, Laina I, DiPietro R, et al. Learning in an uncertain world: Representing ambiguity through multiple hypotheses[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 3591-3600.

Huo Z, Gu B, Huang H. Training neural networks using features replay[C]//Advances in Neural Information Processing Systems. 2018: 6659-6668.

Zamir A R, Sax A, Shen W, et al. Taskonomy: Disentangling Task Transfer Learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3712-3722.

Keyword [Pre-activation] [Identity Mapping]

He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks[C]//European Conference on Computer Vision. Springer, Cham, 2016: 630-645.

Keyword [CliqueNet]

Yang Y, Zhong Z, Shen T, et al. Convolutional Neural Networks with Alternately Updated Clique[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 2413-2422.

Yang W, Li S, Ouyang W, et al. Learning feature pyramids for human pose estimation[C]//The IEEE International Conference on Computer Vision (ICCV). 2017, 2.

Keyword [Spiking ResNet]

Hu Y , Tang H , Wang Y , et al. Spiking Deep Residual Network[J]. 2018.

Keyword [ResNeXt]

Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. IEEE, 2017: 5987-5995.

Kohl S, Romera-Paredes B, Meyer C, et al. A probabilistic u-net for segmentation of ambiguous images[C]//Advances in Neural Information Processing Systems. 2018: 6965-6975.

Keyword [Cerebral Blood Flow]

Ulas C, Tetteh G, Kaczmarz S, et al. DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018: 30-38.