Subhankar Roy

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 251 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #19,705 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 64 citations into it; #34,756 of 35,345 by trust here, #26,682 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Radiology, Nuclear Medicine and Imaging — 23 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 11 citations into it
• Meta & Continual Learning — 9 citations into it
• Generative Models — 5 citations into it

Whom Subhankar Roy trusts overall

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

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

Who trusts Subhankar Roy overall

1. Aliaksandr Siarohin
2. Stéphane Lathuilière
3. K. J. Joseph

Papers from Subhankar Roy's perspective

Ordered by how strongly the researchers Subhankar Roy 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Neighborhood Contrastive Learning for Novel Class Discovery (2021) — 202 citations
• Class-incremental Novel Class Discovery (2022) — 64 citations
• Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020) — 462 citations
• Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification (2018) — 34 citations