Jingwen Ye

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 914 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 244 citations into it; #10,724 of 21,575 by trust here, #8,409 by citations
• Artificial Intelligence — 176 citations into it; #17,054 of 35,345 by trust here, #20,836 by citations
• Representation & Self-Supervised Learning — 45 citations into it
• Efficiency & Compression — 34 citations into it
• Generative Models — 34 citations into it
• Model Architectures — 22 citations into it
• Computer Graphics and Computer-Aided Design — 21 citations into it
• Signal Processing — 19 citations into it

Whom Jingwen Ye trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Karen Simonyan
8. Ilya Sutskever
9. Ross B. Girshick
10. Andrew Zisserman

Who trusts Jingwen Ye overall

1. Lin Wang
2. Xinchao Wang
3. Kuk-Jin Yoon
4. Chengchao Shen
5. Dongze Lian
6. Jianping Gou
7. Mingli Song
8. Yiding Yang
9. Yuang Liu
10. Mengqi Xue

Papers from Jingwen Ye's perspective

Ordered by how strongly the researchers Jingwen Ye trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Neural Style Transfer: A Review (2017) — 870 citations
• Learning with Recoverable Forgetting (2022) — 53 citations
• Factorizing Knowledge in Neural Networks (2022) — 136 citations
• Safe Distillation Box (2021) — 14 citations
• Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN (2020) — 59 citations
• Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers (2019) — 33 citations
• Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More (2019) — 86 citations