Xinyuan Chang

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 6 papers; trusts 447 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it; #5,106 of 21,575 by trust here, #4,137 by citations
• Artificial Intelligence — 124 citations into it; #6,849 of 35,345 by trust here, #5,600 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Meta & Continual Learning — 42 citations into it; #175 of 1,041 by trust here, #121 by citations
• Efficiency & Compression — 22 citations into it
• Radiology, Nuclear Medicine and Imaging — 15 citations into it; #973 of 3,265 by trust here, #1,183 by citations
• Optimization & Training — 14 citations into it
• Reinforcement Learning — 14 citations into it

Whom Xinyuan Chang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Ross B. Girshick
5. Yoshua Bengio
6. Ilya Sutskever
7. Oriol Vinyals
8. Yann LeCun
9. Shaoqing Ren
10. Diederik P. Kingma

Who trusts Xinyuan Chang overall

1. Da-Wei Zhou
2. Tyler L. Hayes
3. Liyuan Wang
4. Songlin Dong
5. Xiaopeng Hong
6. Han-Jia Ye
7. Xiaoyu Tao
8. Kuilin Chen
9. Hanbin Zhao
10. Christopher Kanan

Papers from Xinyuan Chang's perspective

Ordered by how strongly the researchers Xinyuan Chang 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Few-Shot Class-Incremental Learning (2020) — 556 citations
• Topology-Preserving Class-Incremental Learning (2020) — 203 citations
• Few-Shot Class-Incremental Learning via Relation Knowledge Distillation (2021) — 224 citations
• Class-Incremental Learning with Topological Schemas of Memory Spaces (2021) — 5 citations
• Analogy-Detail Networks for Object Recognition (2020) — 9 citations
• Bi-Objective Continual Learning: Learning 'New' While Consolidating 'Known' (2020) — 31 citations