À propos de cet article

Citez

Lu H, Li Y, Chen M, et al. Brain intelligence: go beyond artificial intelligence [J]. Mobile Networks and Applications, 2018, 23(2): 368–375. LuH LiY ChenM Brain intelligence: go beyond artificial intelligence [J] Mobile Networks and Applications 2018 23 2 368 375 Search in Google Scholar

Lu H, Zhang M, Xu X, et al. Deep fuzzy hashing network for efficient image retrieval [J]. IEEE Transactions on Fuzzy Systems, 2020. LuH ZhangM XuX Deep fuzzy hashing network for efficient image retrieval [J] IEEE Transactions on Fuzzy Systems 2020 Search in Google Scholar

Lu, H., Member, S., Tang, Y., & Sun, Y. (2020). DRRS-BC: Decentralized Routing registration system based on blockchain. IEEE/CAA Journal of Automatica Sinica, 1–9. LuH. MemberS. TangY. SunY. 2020 DRRS-BC: Decentralized Routing registration system based on blockchain IEEE/CAA Journal of Automatica Sinica 1 9 Search in Google Scholar

Lu H, Zhang Y, Li Y, et al. User-oriented virtual mobile network resource management for vehicle communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. LuH ZhangY LiY User-oriented virtual mobile network resource management for vehicle communications[J] IEEE Transactions on Intelligent Transportation Systems 2020 Search in Google Scholar

Lu H, Li Y, Mu S, et al. Motor anomaly detection for unmanned aerial vehicles using reinforcement learning [J]. IEEE internet of things journal, 2017, 5(4): 2315–2322. LuH LiY MuS Motor anomaly detection for unmanned aerial vehicles using reinforcement learning [J] IEEE internet of things journal 2017 5 4 2315 2322 Search in Google Scholar

Chen Z, Lu H, Tian S, et al. Construction of a Hierarchical Feature Enhancement Network and Its Application in Fault Recognition [J]. IEEE Transactions on Industrial Informatics, 2020. ChenZ LuH TianS Construction of a Hierarchical Feature Enhancement Network and Its Application in Fault Recognition [J] IEEE Transactions on Industrial Informatics 2020 Search in Google Scholar

LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. LeCunY BottouL BengioY Gradient-based learning applied to document recognition[J] Proceedings of the IEEE 1998 86 11 2278 2324 Search in Google Scholar

H. Lu, R. Yang, Z. Deng, Y. Zhang, G. Gao, R. Lan, “Chinese image captioning via fuzzy attention-based DenseNet-BiLSTM”, ACM Transactions on Multimedia Computing Communications and Applications, 2020. LuH. YangR. DengZ. ZhangY. GaoG. LanR. “Chinese image captioning via fuzzy attention-based DenseNet-BiLSTM” ACM Transactions on Multimedia Computing Communications and Applications 2020 Search in Google Scholar

Dong Chao, Loy Chen Change, He Kaiming, Tang Xiaoou. Image Super-Resolution Using Deep Convolutional Networks. [J]. IEEE transactions on pattern analysis and machine intelligence,2016,38(2). ChaoDong ChangeLoy Chen HeKaiming TangXiaoou Image Super-Resolution Using Deep Convolutional Networks. [J] IEEE transactions on pattern analysis and machine intelligence 2016 38 2 Search in Google Scholar

Shi W, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1874–1883. ShiW CaballeroJ HuszárF Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2016 1874 1883 Search in Google Scholar

Kim J, Kwon Lee J, Mu Lee K. Deeply-recursive convolutional network for image super-resolution[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1637–1645. KimJ Kwon LeeJ Mu LeeK Deeply-recursive convolutional network for image super-resolution[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2016 1637 1645 Search in Google Scholar

Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681–4690. LedigC TheisL HuszárF Photo-realistic single image super-resolution using a generative adversarial network[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2017 4681 4690 Search in Google Scholar

Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in neural information processing systems. 2014: 2672–2680. GoodfellowI Pouget-AbadieJ MirzaM Generative adversarial nets[C] Advances in neural information processing systems 2014 2672 2680 Search in Google Scholar

Wang X, Yu K, Wu S, et al. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 0–0. WangX YuK WuS Esrgan: Enhanced super-resolution generative adversarial networks[C] Proceedings of the European Conference on Computer Vision (ECCV) 2018 0 0 Search in Google Scholar

Kim J, Kwon Lee J, Mu Lee K. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1646–1654. KimJ Kwon LeeJ Mu LeeK Accurate image super-resolution using very deep convolutional networks[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2016 1646 1654 Search in Google Scholar

Wang X, Yu K, Dong C, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 606–615. WangX YuK DongC Recovering realistic texture in image super-resolution by deep spatial feature transform[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2018 606 615 Search in Google Scholar

Timofte R, Rothe R, Van Gool L. Seven ways to improve example-based single image super resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1865–1873. TimofteR RotheR Van GoolL Seven ways to improve example-based single image super resolution[C] Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016 1865 1873 Search in Google Scholar

Zhang Y, Tian Y, Kong Y, et al. Residual dense network for image super-resolution[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2472–2481. ZhangY TianY KongY Residual dense network for image super-resolution[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2018 2472 2481 Search in Google Scholar

Guo Y, Chen J, Wang J, et al. Closed-loop matters: Dual regression networks for single image super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 5407–5416. GuoY ChenJ WangJ Closed-loop matters: Dual regression networks for single image super-resolution[C] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 5407 5416 Search in Google Scholar

Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in neural information processing systems. 2017: 3856–3866. SabourS FrosstN HintonG E Dynamic routing between capsules[C] Advances in neural information processing systems 2017 3856 3866 Search in Google Scholar

Jaiswal A, AbdAlmageed W, Wu Y, et al. Capsulegan: Generative adversarial capsule network[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 0–0. JaiswalA AbdAlmageedW WuY Capsulegan: Generative adversarial capsule network[C] Proceedings of the European Conference on Computer Vision (ECCV) 2018 0 0 Search in Google Scholar

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. SimonyanK ZissermanA Very deep convolutional networks for large-scale image recognition[J] arXiv preprint arXiv:1409.1556 2014 Search in Google Scholar

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770–778. HeK ZhangX RenS Deep residual learning for image recognition[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2016 770 778 Search in Google Scholar

Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700–4708. HuangG LiuZ Van Der MaatenL Densely connected convolutional networks[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2017 4700 4708 Search in Google Scholar

Arjovsky M, Bottou L. Towards principled methods for training generative adversarial networks [J]. arXiv preprint arXiv:1701.04862, 2017. ArjovskyM BottouL Towards principled methods for training generative adversarial networks [J] arXiv preprint arXiv:1701.04862 2017 Search in Google Scholar

Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017. ArjovskyM ChintalaS BottouL Wasserstein gan[J] arXiv preprint arXiv:1701.07875 2017 Search in Google Scholar

Cao J, Mo L, Zhang Y, et al. Multi-marginal wasserstein gan [J]. arXiv preprint arXiv:1911.00888, 2019. CaoJ MoL ZhangY Multi-marginal wasserstein gan [J] arXiv preprint arXiv:1911.00888 2019 Search in Google Scholar

eISSN:
2470-8038
Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Computer Sciences, other