Guanghua Gu

Computer Vision and Pattern Recognition · 2023–2024

A researcher in the Science corpus: 3 papers; trusts 150 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it
• Generative Models — 25 citations into it
• Artificial Intelligence — 18 citations into it
• Model Architectures — 6 citations into it

Whom Guanghua Gu trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Karen Simonyan
4. Tero Karras
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Jimmy Ba
8. Christian Szegedy
9. Yoshua Bengio
10. Andrew Zisserman

Papers from Guanghua Gu's perspective

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

Their papers

• Rethinking the Up-Sampling Operations in CNN-Based Generative Network for Generalizable Deepfake Detection (2024) — 392 citations
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning (2024) — 299 citations
• Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection (2023) — 296 citations