Baochen Sun

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 7 papers; trusts 1,098 researchers and is trusted by 2,312 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 129 citations into it; #808 of 21,575 by trust here, #1,641 by citations
• Artificial Intelligence — 79 citations into it; #847 of 35,345 by trust here, #2,219 by citations
• Representation & Self-Supervised Learning — 48 citations into it; #284 of 3,145 by trust here, #745 by citations
• Evaluation & Benchmarks — 21 citations into it
• Aerospace Engineering — 13 citations into it
• Signal Processing — 10 citations into it
• Cancer Research — 9 citations into it; #40 of 1,780 by trust here, #505 by citations
• Media Technology — 8 citations into it

Whom Baochen Sun trusts overall

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

1. Alex Krizhevsky
2. Kate Saenko
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Jia Deng
6. Ross B. Girshick
7. Kaiming He
8. Yann LeCun
9. Yoshua Bengio
10. Andrew Zisserman

Who trusts Baochen Sun overall

1. Xingchao Peng
2. Gabriella Csurka
3. Kate Saenko
4. Garrett Wilson
5. Jindong Wang
6. Kuniaki Saito
7. Eric Tzeng
8. Judy Hoffman
9. Yang Zhang
10. Jiashi Feng

Papers from Baochen Sun's perspective

Ordered by how strongly the researchers Baochen Sun 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Return of Frustratingly Easy Domain Adaptation (2015) — 2,086 citations
• Learning Deep Object Detectors from 3D Models (2014) — 395 citations
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation (2016) — 3,996 citations
• From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains (2014) — 169 citations
• Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images (2014) — 59 citations
• Correlation Alignment for Unsupervised Domain Adaptation (2016) — 512 citations
• Checkerboard Context Model for Efficient Learned Image Compression (2021) — 412 citations