Bingchen Liu

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 536 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 126 citations into it; #12,839 of 21,575 by trust here, #7,852 by citations
• Generative Models — 89 citations into it; #1,406 of 1,956 by trust here, #1,085 by citations
• Artificial Intelligence — 57 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it
• Media Technology — 8 citations into it
• Model Architectures — 8 citations into it
• Signal Processing — 5 citations into it

Whom Bingchen Liu trusts overall

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

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

Who trusts Bingchen Liu overall

1. Axel Sauer
2. Yang Song
3. Qinsheng Zhang
4. Sam Bond-Taylor
5. Zhendong Wang
6. Prafulla Dhariwal
7. Yongxin Chen
8. Eva Cetinic
9. Jean-Pierre Briot
10. Rowan T. Hughes

Papers from Bingchen Liu's perspective

Ordered by how strongly the researchers Bingchen Liu 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. GENERATIVE ADVERSARIAL NETS

Their papers

• A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts (2017) — 402 citations
• Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (2021) — 316 citations
• CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms (2017) — 604 citations
• Shifted Diffusion for Text-to-image Generation (2022) — 61 citations
• Common Diffusion Noise Schedules and Sample Steps are Flawed (2023) — 364 citations