Yifan Zhang

Artificial Intelligence · with Tan · 2019–2023

A researcher in the Science corpus: 11 papers; trusts 966 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 409 citations into it; #26,212 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 371 citations into it; #16,170 of 21,575 by trust here, #8,831 by citations
• Representation & Self-Supervised Learning — 182 citations into it
• Radiology, Nuclear Medicine and Imaging — 57 citations into it
• Optimization & Training — 54 citations into it
• Evaluation & Benchmarks — 42 citations into it
• Model Architectures — 23 citations into it
• Federated & Distributed Learning — 21 citations into it

Whom Yifan Zhang trusts overall

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

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

Who trusts Yifan Zhang overall

1. Shuaicheng Niu
2. Hongbin Lin
3. Zhen Qiu
4. Jiaxiang Wu
5. Bryan Hooi
6. Qizhengqiu Lu
7. Hee E. Kim
8. Mingkui Tan
9. Da-Wei Zhou
10. Dapeng Hu

Papers from Yifan Zhang's perspective

Ordered by how strongly the researchers Yifan Zhang 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. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021) — 39 citations
• Deep Long-Tailed Learning: A Survey (2021) — 927 citations
• Towards Stable Test-Time Adaptation in Dynamic Wild World (2023) — 531 citations
• Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (2022) — 59 citations
• Efficient Test-Time Model Adaptation without Forgetting (2022) — 647 citations
• How Well Self-Supervised Pre-Training Performs with Streaming Data? (2021) — 12 citations
• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (2021) — 81 citations
• Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation (2021) — 212 citations
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data (2021) — 483 citations
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (2019) — 188 citations
• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019) — 70 citations