Hyo-Eun Kim

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 367 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #7,882 of 21,575 by trust here, #12,419 by citations
• Artificial Intelligence — 31 citations into it; #11,768 of 35,345 by trust here, #18,586 by citations
• Model Architectures — 14 citations into it; #1,304 of 2,278 by trust here, #1,940 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Optimization & Training — 8 citations into it
• Generative Models — 6 citations into it
• Efficiency & Compression — 5 citations into it

Whom Hyo-Eun Kim 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. Yann LeCun
6. Yoshua Bengio
7. Sergey Ioffe
8. Ilya Sutskever
9. Christian Szegedy
10. Diederik P. Kingma

Who trusts Hyo-Eun Kim overall

1. Kibok Lee
2. Tameem Adel
3. Kimin Lee
4. Inyoung Paik
5. Hyeonseob Nam
6. Zheng Xu
7. Jinwoo Shin
8. Alexander Rainer Tassilo Gepperth
9. Jianping Gou
10. Han Zhao

Papers from Hyo-Eun Kim's perspective

Ordered by how strongly the researchers Hyo-Eun Kim 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks (2018) — 31 citations
• SRM: A Style-Based Recalibration Module for Convolutional Neural Networks (2019) — 294 citations
• Transferring Knowledge to Smaller Network with Class-Distance Loss (2017) — 45 citations