Animesh Karnewar

Generative Models · 2019

A researcher in the Science corpus: 2 papers; trusts 412 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Generative Models — 63 citations into it; #694 of 1,956 by trust here, #1,460 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #7,276 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 18 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it
• Optimization & Training — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Animesh Karnewar trusts overall

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

1. Diederik P. Kingma
2. Tero Karras
3. Aäron van den Oord
4. Laurent Dinh
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Danilo Jimenez Rezende
8. Alex Krizhevsky
9. Kaiming He
10. Yann LeCun

Who trusts Animesh Karnewar overall

1. Shiyu Chang
2. Gaurav Parmar
3. Lintao Peng
4. Bingchen Liu
5. Håkon Hukkelås
6. Jooyoung Choi
7. Tero Karras
8. Zhangyang Wang
9. Richard Zhang
10. Jian Zhao

Papers from Animesh Karnewar's perspective

Ordered by how strongly the researchers Animesh Karnewar trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis (2019) — 50 citations
• MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks (2019) — 224 citations