Sangwoo Mo

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 670 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 138 citations into it; #12,164 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 105 citations into it; #19,201 of 35,345 by trust here, #17,776 by citations
• Generative Models — 52 citations into it; #1,111 of 1,956 by trust here, #1,435 by citations
• Efficiency & Compression — 30 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #1,966 of 3,145 by trust here, #2,299 by citations
• Optimization & Training — 19 citations into it
• Model Architectures — 16 citations into it
• Safety, Robustness & Interpretability — 14 citations into it

Whom Sangwoo Mo trusts overall

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

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

Who trusts Sangwoo Mo overall

1. Gyuhak Kim
2. Miaoyun Zhao
3. Peihao Zhu
4. Bingchen Liu
5. Yingxue Pang
6. Yufan Zhou
7. Pei-Hung Chen
8. Esther Robb
9. Hung-Yu Tseng
10. Shengyu Zhao

Papers from Sangwoo Mo's perspective

Ordered by how strongly the researchers Sangwoo Mo 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs (2020) — 235 citations
• InstaGAN: Instance-aware Image-to-Image Translation (2018) — 168 citations
• CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (2020) — 745 citations
• Layer-adaptive Sparsity for the Magnitude-based Pruning (2020) — 354 citations