Arpit Jain

Computer Vision and Pattern Recognition · with Chellappa · 2009–2017

A researcher in the Science corpus: 4 papers; trusts 341 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #5,753 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 48 citations into it; #20,923 of 35,345 by trust here, #11,327 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it
• Biomedical Engineering — 5 citations into it

Whom Arpit Jain trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ilya Sutskever
8. Christian Szegedy
9. Andrew Zisserman
10. Yann LeCun

Who trusts Arpit Jain overall

1. Carl Doersch
2. Atsushi Kanehira
3. Cewu Lu
4. Abhinav Kumar Gupta
5. Ruohan Gao
6. Luc Van Gool
7. Bharat Singh
8. Bálint Antal
9. Alexei A. Efros
10. Kuniaki Saito

Papers from Arpit Jain's perspective

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

Their papers

• Representing Videos Using Mid-level Discriminative Patches (2013) — 155 citations
• Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation (2017) — 527 citations
• Regularizing deep networks using efficient layerwise adversarial training (2017) — 103 citations
• Automated feature extraction for early detection of diabetic retinopathy in fundus images (2009) — 324 citations