Adrien Bardes

Computer Vision and Pattern Recognition · 2021–2025

A researcher in the Science corpus: 4 papers; trusts 780 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 297 citations into it; #13,848 of 21,575 by trust here, #10,334 by citations
• Artificial Intelligence — 279 citations into it; #22,457 of 35,345 by trust here, #16,166 by citations
• Representation & Self-Supervised Learning — 161 citations into it; #2,212 of 3,145 by trust here, #2,042 by citations
• Model Architectures — 34 citations into it
• Optimization & Training — 25 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Signal Processing — 23 citations into it
• Generative Models — 16 citations into it

Whom Adrien Bardes trusts overall

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

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

Who trusts Adrien Bardes overall

1. Victor Costa
2. Enrico Fini
3. Li Jing
4. Yi Wang
5. Mahmoud Assran
6. Christian Simon
7. Yujun Shi
8. Randall Balestriero
9. Pascal Vincent
10. Qingwen Bu

Papers from Adrien Bardes's perspective

Ordered by how strongly the researchers Adrien Bardes 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning (2021) — 1,331 citations
• Revisiting Feature Prediction for Learning Visual Representations from Video (2024) — 396 citations
• A Cookbook of Self-Supervised Learning (2023) — 398 citations
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025) — 527 citations