Jogendra Nath Kundu

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 548 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 120 citations into it
• Artificial Intelligence — 118 citations into it
• Representation & Self-Supervised Learning — 58 citations into it
• Optimization & Training — 14 citations into it
• Generative Models — 12 citations into it
• Cancer Research — 9 citations into it
• Model Architectures — 7 citations into it

Whom Jogendra Nath Kundu trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Ilya Sutskever
9. Shaoqing Ren
10. Mingsheng Long

Who trusts Jogendra Nath Kundu overall

1. Shuaicheng Niu
2. Shiqi Yang
3. Kuniaki Saito
4. Yuang Liu
5. Dequan Wang
6. Yifan Zhang
7. Jiaxiang Wu
8. Hongbin Lin
9. Yaxing Wang
10. Stan Sclaroff

Papers from Jogendra Nath Kundu's perspective

Ordered by how strongly the researchers Jogendra Nath Kundu 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Class-Incremental Domain Adaptation (2020) — 58 citations
• Universal Source-Free Domain Adaptation (2020) — 404 citations
• UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation (2019) — 62 citations