Xingchao Peng

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 9 papers; trusts 1,360 researchers and is trusted by 475 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 301 citations into it; #1,947 of 21,575 by trust here, #2,287 by citations
• Artificial Intelligence — 211 citations into it; #4,153 of 35,345 by trust here, #3,035 by citations
• Representation & Self-Supervised Learning — 114 citations into it; #1,057 of 3,145 by trust here, #682 by citations
• Evaluation & Benchmarks — 54 citations into it
• Generative Models — 49 citations into it
• Aerospace Engineering — 20 citations into it
• Cancer Research — 18 citations into it; #379 of 1,780 by trust here, #621 by citations
• Media Technology — 17 citations into it

Whom Xingchao Peng trusts overall

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

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

Who trusts Xingchao Peng overall

1. Jian Liang
2. Kuniaki Saito
3. Dapeng Hu
4. Yogesh Kumar Balaji
5. Kate Saenko
6. Ying Jin
7. Gabriella Csurka
8. Trevor Darrell
9. Peng Su
10. Eric Tzeng

Papers from Xingchao Peng's perspective

Ordered by how strongly the researchers Xingchao Peng 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Deep Object Detectors from 3D Models (2014) — 395 citations
• Moment Matching for Multi-Source Domain Adaptation (2018) — 2,338 citations
• VisDA: The Visual Domain Adaptation Challenge (2017) — 929 citations
• Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments (2015) — 105 citations
• Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images (2014) — 59 citations
• VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation (2018) — 206 citations
• Domain Agnostic Learning with Disentangled Representations (2019) — 304 citations
• Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation (2020) — 41 citations
• Federated Adversarial Domain Adaptation (2019) — 319 citations