Maneesh Kumar Singh

Computer Vision and Pattern Recognition · 2008–2019

A researcher in the Science corpus: 8 papers; trusts 927 researchers and is trusted by 290 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 208 citations into it; #2,354 of 21,575 by trust here, #3,089 by citations
• Artificial Intelligence — 103 citations into it; #28,879 of 35,345 by trust here, #20,377 by citations
• Generative Models — 72 citations into it; #1,132 of 1,956 by trust here, #759 by citations
• Representation & Self-Supervised Learning — 52 citations into it; #1,077 of 3,145 by trust here, #955 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 16 citations into it
• Signal Processing — 14 citations into it
• Radiology, Nuclear Medicine and Imaging — 13 citations into it

Whom Maneesh Kumar Singh trusts overall

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

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

Who trusts Maneesh Kumar Singh overall

1. Aviv Gabbay
2. Longlong Jing
3. Kevin Scaman
4. Henry Gouk
5. Yang Zhao
6. Hsin-Ying Lee
7. Dahun Kim
8. Spyros Gidaris
9. Hung-Yu Tseng
10. Jiangliu Wang

Papers from Maneesh Kumar Singh's perspective

Ordered by how strongly the researchers Maneesh Kumar Singh 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. Going deeper with convolutions

Their papers

• Unsupervised Representation Learning by Sorting Sequences (2017) — 570 citations
• Diverse Image-to-Image Translation via Disentangled Representations (2018) — 1,049 citations
• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations (2019) — 804 citations
• Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders (2018) — 137 citations
• Progressive Domain Adaptation for Object Detection (2019) — 369 citations
• On Lipschitz Bounds of General Convolutional Neural Networks (2018) — 60 citations
• Lipschitz Properties for Deep Convolutional Networks (2017) — 52 citations
• State of Art of Saffron (Crocus sativus L.) Agronomy: A Comprehensive Review (2008) — 170 citations