Jun Seo

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 213 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #17,272 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #10,917 of 21,575 by trust here, #11,950 by citations
• Meta & Continual Learning — 34 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 8 citations into it
• Model Architectures — 5 citations into it

Whom Jun Seo trusts overall

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

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

Who trusts Jun Seo overall

1. Steinar Laenen
2. Sha Li
3. Do-Yeon Kim
4. Christian Simon
5. Song Tian
6. Sung Whan Yoon
7. Afra Feyza Akyürek
8. Yutai Hou

Papers from Jun Seo's perspective

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

Their papers

• XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (2020) — 49 citations
• TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (2019) — 302 citations
• Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning (2023) — 75 citations