Supasorn Suwajanakorn
Computer Vision and Pattern Recognition · 2014–2021
A researcher in the Science corpus: 6 papers; trusts 837 researchers and is trusted by 109 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 213 citations into it; #3,162 of 21,575 by trust here, #4,469 by citations
- Generative Models — 72 citations into it; #1,413 of 1,956 by trust here, #1,355 by citations
- Artificial Intelligence — 58 citations into it
- Computer Graphics and Computer-Aided Design — 30 citations into it; #1,582 of 1,584 by trust here, #875 by citations
- Signal Processing — 21 citations into it; #4,219 of 7,572 by trust here, #2,664 by citations
- Representation & Self-Supervised Learning — 18 citations into it
- Model Architectures — 15 citations into it
- Evaluation & Benchmarks — 14 citations into it
Whom Supasorn Suwajanakorn trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Alex Graves
- Yann LeCun
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- J. Urgen Schmidhuber
- Karen Simonyan
Who trusts Supasorn Suwajanakorn overall
- Michael Zollhöfer
- Justus Thies
- Anh Tuan Tran
- Pablo Garrido
- Hyeongwoo Kim
- Yisroel Mirsky
- Cristian Vaccari
- Ayush Kumar Tewari
- Yue Wang
- Tal Hassner
Papers from Supasorn Suwajanakorn's perspective
Ordered by how strongly the researchers Supasorn Suwajanakorn trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning (2018) — 261 citations
- Total Moving Face Reconstruction (2014) — 193 citations
- Diffusion Autoencoders: Toward a Meaningful and Decodable Representation (2021) — 614 citations
- Repurposing GANs for One-Shot Semantic Part Segmentation (2021) — 127 citations
- Synthesizing Obama (2017) — 571 citations
- NeX: Real-time View Synthesis with Neural Basis Expansion (2021) — 355 citations