Daiki Ikami

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 377 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it; #12,384 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 55 citations into it; #19,814 of 35,345 by trust here, #12,580 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Daiki Ikami trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Christian Szegedy
8. Diederik P. Kingma
9. Ross B. Girshick
10. X. Zhang

Who trusts Daiki Ikami overall

1. Junnan Li
2. Pengfei Chen
3. Görkem Algan
4. Dongkai Wang
5. Haiyang Zhang
6. Peixiang Zhong
7. Hongxin Wei
8. Hrayr Harutyunyan
9. Elad Amrani
10. Youngdong Kim

Papers from Daiki Ikami's perspective

Ordered by how strongly the researchers Daiki Ikami 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Joint Optimization Framework for Learning with Noisy Labels (2018) — 806 citations
• Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features (2017) — 70 citations
• Learning with Selective Forgetting (2021) — 82 citations