Hideaki Hayashi

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it
• Generative Models — 18 citations into it

Whom Hideaki Hayashi trusts overall

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

1. Alec Radford
2. Individualized Treat
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Tim Salimans
6. Martín Arjovsky
7. Ian J. Goodfellow
8. Yoshua Bengio
9. Kaiming He
10. Fausto Milletarì

Papers from Hideaki Hayashi's perspective

Ordered by how strongly the researchers Hideaki Hayashi 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. GENERATIVE ADVERSARIAL NETS
4. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Improved Techniques for Training GANs
8. Long Short-Term Memory
9. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection (2019) — 100 citations
• GAN-based synthetic brain MR image generation (2018) — 360 citations