Chenglong Bao

Computer Vision and Pattern Recognition · with Ma · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 838 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it
• Artificial Intelligence — 147 citations into it
• Efficiency & Compression — 74 citations into it
• Model Architectures — 46 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Optimization & Training — 19 citations into it
• Meta & Continual Learning — 13 citations into it
• Media Technology — 8 citations into it

Whom Chenglong Bao trusts overall

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

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

Papers from Chenglong Bao's perspective

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

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

Their papers

• Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation (2019) — 1,120 citations
• AFEC: Active Forgetting of Negative Transfer in Continual Learning (2021) — 123 citations
• Self-Distillation: Towards Efficient and Compact Neural Networks (2021) — 319 citations
• Task-Oriented Feature Distillation (2020) — 73 citations