Sudipan Saha
Computer Vision and Pattern Recognition · 2019–2022
A researcher in the Science corpus: 2 papers; trusts 179 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 44 citations into it
- Media Technology — 23 citations into it; #2,092 of 3,621 by trust here, #2,479 by citations
- Artificial Intelligence — 22 citations into it; #22,137 of 35,345 by trust here, #23,077 by citations
- Representation & Self-Supervised Learning — 13 citations into it
- Atmospheric Science — 8 citations into it; #2,343 of 4,804 by trust here, #3,434 by citations
- Ocean Engineering — 7 citations into it
- Aerospace Engineering — 5 citations into it
- Model Architectures — 5 citations into it
Whom Sudipan Saha trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Radford M. Neal
- Geoffrey E. Hinton
- Karen Simonyan
- Alex Krizhevsky
- Ross B. Girshick
- Francesca Bovolo
- Yann LeCun
- Lorenzo Bruzzone
- Yoshua Bengio
Who trusts Sudipan Saha overall
- Hongruixuan Chen
- Yuxing Chen
- Valerio Marsocci
- Lorenzo Bruzzone
- Daifeng Peng
- Yuchao Feng
- Ayesha Shafique
- Ting Bai
- Yi Wang
- Le Wang
Papers from Sudipan Saha's perspective
Ordered by how strongly the researchers Sudipan Saha trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Unsupervised Single-Scene Semantic Segmentation for Earth Observation (2022) — 41 citations
- Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images (2019) — 489 citations