Sang-Hoon Lee

Artificial Intelligence · with Lee · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 125 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it
• Signal Processing — 112 citations into it; #7,477 of 7,572 by trust here, #6,591 by citations
• Speech & Audio — 105 citations into it
• Generative Models — 58 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Sang-Hoon Lee trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Aäron van den Oord
5. Jimmy Ba
6. Alex Krizhevsky
7. Alex Graves
8. Ilya Sutskever
9. Kaiming He
10. Yann LeCun

Who trusts Sang-Hoon Lee overall

1. Xu Tan
2. Hyung-Seok Oh
3. Zeqian Ju
4. Ji-Hoon Kim
5. Yanqing Liu
6. Tao Qin
7. Edresson Casanova
8. Rui Xue
9. Frank K. Soong
10. Yuancheng Wang

Papers from Sang-Hoon Lee's perspective

Ordered by how strongly the researchers Sang-Hoon Lee 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis (2020) — 60 citations
• Fre-GAN: Adversarial Frequency-consistent Audio Synthesis (2021) — 61 citations
• HierSpeech++: Bridging the Gap Between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-Shot Speech Synthesis (2023) — 78 citations
• DiffProsody: Diffusion-Based Latent Prosody Generation for Expressive Speech Synthesis With Prosody Conditional Adversarial Training (2023) — 35 citations
• HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis (2022) — 81 citations