| Towards Optimal Structured CNN Pruning via Generative Adversarial Learning S Lin, R Ji, C Yan, B Zhang, L Cao, Q Ye, F Huang, D Doermann Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019 | 546 | 2019 |
| Contrastive Learning for Compact Single Image Dehazing H Wu, Y Qu, S Lin, J Zhou, R Qiao, Z Zhang, Y Xie, L Ma CVPR, 2021 | 426 | 2021 |
| Accelerating Convolutional Networks via Global & Dynamic Filter Pruning S Lin, R Ji, Y Li, Y Wu, F Huang, B Zhang IJCAI, 2425-2432, 2018 | 252 | 2018 |
| Toward compact convnets via structure-sparsity regularized filter pruning S Lin, R Ji, Y Li, C Deng, X Li IEEE transactions on neural networks and learning systems 31 (2), 574-588, 2019 | 173 | 2019 |
| Holistic cnn compression via low-rank decomposition with knowledge transfer S Lin, R Ji, C Chen, D Tao, J Luo IEEE transactions on pattern analysis and machine intelligence 41 (12), 2889 …, 2018 | 150 | 2018 |
| Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression Y Li, S Lin, B Zhang, J Liu, D Doermann, Y Wu, F Huang, R Ji Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019 | 145 | 2019 |
| Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification X Tian, Z Zhang, S Lin, Y Qu, Y Xie, L Ma CVPR (oral), 2021 | 88 | 2021 |
| Towards Compact CNNs via Collaborative Compression Y Li, S Lin, J Liu, Q Ye, M Wang, F Chao, F Yang, J Ma, Q Tian, R Ji CVPR, 2021 | 69 | 2021 |
| Masked face detection via a modified LeNet S Lin, L Cai, X Lin, R Ji Neurocomputing 218, 197-202, 2016 | 64 | 2016 |
| PAMS: Quantized Super-Resolution via Parameterized Max Scale H Li, C Yan, S Lin, X Zheng, Y Li, B Zhang, F Yang, R Ji ECCV, 2020 | 62 | 2020 |
| Towards Convolutional Neural Networks Compression via Global Error Reconstruction S Lin, R Ji, X Guo, X Li Proceedings of the Twenty-Fifth International Joint Conference on Artificial …, 2016 | 53 | 2016 |
| Towards Compact Single Image Super-Resolution via Contrastive Self-distillation Y Wang, S Lin, Y Qu, H Wu, Z Zhang, Y Xie, A Yao IJCAI, 2021 | 40 | 2021 |
| HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization M Li, Y Xie, Y Shen, B Ke, R Qiao, B Ren, S Lin, L Ma | 36 | 2022 |
| Comprehensive regularization in a bi-directional predictive network for video anomaly detection C Chen, Y Xie, S Lin, A Yao, G Jiang, W Zhang, Y Qu, R Qiao, B Ren, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (1), 230-238, 2022 | 35 | 2022 |
| Disco: Remedy self-supervised learning on lightweight models with distilled contrastive learning Y Gao, JX Zhuang, S Lin, H Cheng, X Sun, K Li, C Shen ECCV（oral）, 2022 | 28 | 2022 |
| ESPACE: Accelerating Convolutional Neural Networks via Eliminating Spatial & Channel Redundancy S Lin, R Ji, C Chen, F Huang Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence …, 2017 | 25 | 2017 |
| Pruning blocks for CNN compression and acceleration via online ensemble distillation Z Wang, S Lin, J Xie, Y Lin IEEE Access 7, 175703-175716, 2019 | 23 | 2019 |
| Deep neural network compression and acceleration: A review J Rongrong, L Shaohui, C Fei, W Yongjian, H Feiyue Journal of computer research and development 55 (9), 1871, 2018 | 19* | 2018 |
| Compressing convolutional neural networks with cheap convolutions and online distillation J Xie, S Lin, Y Zhang, L Luo Displays 78, 102428, 2023 | 12* | 2023 |
| Self-supervised models are good teaching assistants for vision transformers H Wu, Y Gao, Y Zhang, S Lin, Y Xie, X Sun, K Li International Conference on Machine Learning, 24031-24042, 2022 | 11 | 2022 |
Rongrong Ji 纪荣嵘Professor, Xiamen UniversityVerified email at xmu.edu.cn
