Utkarsh Ojha

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 861 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 159 citations into it; #15,812 of 21,575 by trust here, #10,500 by citations
• Generative Models — 79 citations into it; #1,497 of 1,956 by trust here, #1,549 by citations
• Artificial Intelligence — 58 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Computer Graphics and Computer-Aided Design — 13 citations into it
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Media Technology — 11 citations into it

Whom Utkarsh Ojha trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Jia Deng

Who trusts Utkarsh Ojha overall

1. Yuheng Li
2. Max Vargas
3. Juwon Seo
4. Rinon Gal
5. Peihao Zhu
6. Han-Jia Ye
7. Ceyuan Yang
8. Yu-Ding Lu
9. Tristan Sylvain
10. Reilly Cannon

Papers from Utkarsh Ojha's perspective

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

Their papers

• Few-shot Image Generation via Cross-domain Correspondence (2021) — 318 citations
• MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation (2019) — 84 citations
• FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery (2018) — 141 citations
• Towards Universal Fake Image Detectors that Generalize Across Generative Models (2023) — 644 citations