Kwang-Ting Cheng

Computer Vision and Pattern Recognition · European Conference on Computer Vision · 2017–2021

A researcher in the Science corpus: 13 papers; trusts 816 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 237 citations into it; #7,353 of 21,575 by trust here, #6,542 by citations
• Artificial Intelligence — 178 citations into it; #11,395 of 35,345 by trust here, #8,893 by citations
• Efficiency & Compression — 84 citations into it
• Radiology, Nuclear Medicine and Imaging — 54 citations into it; #1,499 of 3,265 by trust here, #2,073 by citations
• Model Architectures — 43 citations into it
• Ophthalmology — 37 citations into it; #130 of 222 by trust here, #156 by citations
• Optimization & Training — 31 citations into it
• Representation & Self-Supervised Learning — 29 citations into it

Whom Kwang-Ting Cheng trusts overall

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

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

Who trusts Kwang-Ting Cheng overall

1. Zhiqiang Shen
2. Zechun Liu
3. Jing Liu
4. Yao Zhou
5. Arvid Lundervold

Papers from Kwang-Ting Cheng's perspective

Ordered by how strongly the researchers Kwang-Ting Cheng 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. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (2021) — 24 citations
• Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm (2018) — 648 citations
• MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning (2019) — 640 citations
• Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation (2021) — 167 citations
• Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study (2021) — 83 citations
• Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks (2017) — 154 citations
• The 2021 flexible and printed electronics roadmap (2021) — 154 citations
• ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions (2020) — 434 citations
• Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework (2020) — 44 citations
• Visualizing the decision-making process in deep neural decision forest (2019) — 8 citations
• A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019) — 305 citations
• Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018) — 385 citations
• Co‐trained convolutional neural networks for automated detection of prostate cancer in multi‐parametric MRI (2017) — 143 citations