Jiapeng Zhu

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 921 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 266 citations into it; #3,772 of 21,575 by trust here, #4,887 by citations
• Generative Models — 192 citations into it; #311 of 1,956 by trust here, #522 by citations
• Artificial Intelligence — 95 citations into it
• Representation & Self-Supervised Learning — 39 citations into it; #1,990 of 3,145 by trust here, #2,624 by citations
• Computer Graphics and Computer-Aided Design — 20 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Media Technology — 17 citations into it

Whom Jiapeng Zhu trusts overall

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

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

Who trusts Jiapeng Zhu overall

1. Yujun Shen
2. Yuval Alaluf
3. Yotam Nitzan
4. Weihao Xia
5. Rameen Abdal
6. Qingyan Bai
7. Bolei Zhou
8. Peihao Zhu
9. Zongze Wu
10. Yinghao Xu

Papers from Jiapeng Zhu's perspective

Ordered by how strongly the researchers Jiapeng Zhu 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. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• In-Domain GAN Inversion for Real Image Editing (2020) — 694 citations
• Generative Hierarchical Features from Synthesizing Images (2020) — 126 citations
• High-fidelity GAN Inversion with Padding Space (2022) — 34 citations
• One-Shot Generative Domain Adaptation (2021) — 69 citations
• Low-Rank Subspaces in GANs (2021) — 79 citations
• Disentangled Inference for GANs With Latently Invertible Autoencoder (2019) — 39 citations
• LIA: Latently Invertible Autoencoder with Adversarial Learning (2019) — 21 citations