Yaxing Wang

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 511 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #7,615 of 21,575 by trust here, #4,245 by citations
• Artificial Intelligence — 141 citations into it; #11,667 of 35,345 by trust here, #5,749 by citations
• Generative Models — 62 citations into it; #738 of 1,956 by trust here, #522 by citations
• Representation & Self-Supervised Learning — 52 citations into it
• Optimization & Training — 23 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Natural Language Processing — 15 citations into it
• Model Architectures — 14 citations into it

Whom Yaxing Wang 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. Ilya Sutskever
8. Karen Simonyan
9. Ross B. Girshick
10. Andrew Zisserman

Who trusts Yaxing Wang overall

1. Miaoyun Zhao
2. Yulai Cong
3. Nupur Kumari
4. Hung-Yu Tseng
5. Yijun Li
6. Richard Zhang
7. T. Grigoryev
8. Sangwoo Mo
9. Atsuhiro Noguchi
10. Rinon Gal

Papers from Yaxing Wang's perspective

Ordered by how strongly the researchers Yaxing Wang 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Transferring GANs: generating images from limited data (2018) — 320 citations
• MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images (2019) — 208 citations
• Memory Replay GANs: learning to generate images from new categories without forgetting (2018) — 203 citations
• Does Multimodality Help Human and Machine for Translation and Image Captioning? (2016) — 87 citations
• Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (2021) — 351 citations
• Generalized Source-free Domain Adaptation (2021) — 328 citations