Dipam Goswami

Artificial Intelligence · 2023–2025

A researcher in the Science corpus: 3 papers; trusts 298 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it
• Computer Vision and Pattern Recognition — 109 citations into it
• Meta & Continual Learning — 52 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Efficiency & Compression — 10 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Dipam Goswami trusts overall

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

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

Who trusts Dipam Goswami overall

1. Haoxuan Qu
2. Hossein Rahmani

Papers from Dipam Goswami's perspective

Ordered by how strongly the researchers Dipam Goswami 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models (2025)
• Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning (2024) — 48 citations
• Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection (2023) — 59 citations