Eric I-Chao Chang

Computer Vision and Pattern Recognition · 2012–2021

A researcher in the Science corpus: 8 papers; trusts 568 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 214 citations into it; #8,789 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 73 citations into it; #16,839 of 35,345 by trust here, #10,065 by citations
• Generative Models — 44 citations into it
• Radiology, Nuclear Medicine and Imaging — 26 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Media Technology — 16 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Eric I-Chao Chang trusts overall

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

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

Who trusts Eric I-Chao Chang overall

1. Dingwen Zhang
2. Jiajun Wu
3. Daisuke Komura
4. Quanshi Zhang
5. Tony C. W. Mok

Papers from Eric I-Chao Chang's perspective

Ordered by how strongly the researchers Eric I-Chao Chang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021) — 359 citations
• Unsupervised object class discovery via saliency-guided multiple class learning (2012) — 160 citations
• Deep learning of feature representation with multiple instance learning for medical image analysis (2014) — 415 citations
• Weakly supervised histopathology cancer image segmentation and classification (2014) — 312 citations
• Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019) — 300 citations
• Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge (2018) — 307 citations
• Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017) — 55 citations
• Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features (2017) — 407 citations