Hung-Yu Tseng

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 9 papers; trusts 1,350 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 354 citations into it; #7,318 of 21,575 by trust here, #3,613 by citations
• Generative Models — 185 citations into it; #1,135 of 1,956 by trust here, #569 by citations
• Artificial Intelligence — 169 citations into it
• Representation & Self-Supervised Learning — 62 citations into it
• Optimization & Training — 34 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Radiology, Nuclear Medicine and Imaging — 24 citations into it
• Computer Graphics and Computer-Aided Design — 19 citations into it

Whom Hung-Yu Tseng trusts overall

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

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

Who trusts Hung-Yu Tseng overall

1. Hsin-Ying Lee
2. Han-Jia Ye
3. Yang Zhao
4. Ming-Hsuan Yang
5. Namyeong Kwon
6. Lu Jiang
7. Yisheng Song
8. Yu-Ding Lu
9. Yen-Chi Cheng
10. Oron Ashual

Papers from Hung-Yu Tseng's perspective

Ordered by how strongly the researchers Hung-Yu Tseng 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Diverse Image-to-Image Translation via Disentangled Representations (2018) — 1,049 citations
• Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (2019) — 444 citations
• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations (2019) — 804 citations
• Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (2020) — 479 citations
• Regularizing Generative Adversarial Networks under Limited Data (2021) — 185 citations
• RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval (2020) — 57 citations
• Progressive Domain Adaptation for Object Detection (2019) — 369 citations
• Unsupervised Discovery of Disentangled Manifolds in GANs (2020) — 9 citations
• Text as Neural Operator:Image Manipulation by Text Instruction (2020) — 41 citations