FADE Research Group

223, CSE Department,

Indian Institute of Technology,

Jodhpur, Rajasthan, 342030

The Fairness And Data Efficiency (FADE) Research Group at the Indian Institute of Technology Jodhpur, led by Dr Pratik Mazumder, focuses on developing equitable AI systems and advancing deep learning techniques for data-constrained environments, with applications in bias mitigation and medical image processing.

Note: We are looking for motivated PhD, B.Tech., and M.Tech. students with at least some experience in Deep Learning.

Selected Publications

Leveraging Joint Incremental Learning Objective with Data Ensemble for Class Incremental Learning

Pratik Mazumder, Mohammed Asad Karim, Indu Joshi, and 1 more author

Neural Networks, 2024

Bib

@article{2024_NN_mazumder_leveraging,
  selected = true,
  title = {Leveraging Joint Incremental Learning Objective with Data Ensemble for Class Incremental Learning},
  author = {Mazumder, Pratik and Karim, Mohammed Asad and Joshi, Indu and Singh, Pravendra},
  journal = {Neural Networks},
  year = {2024},
  publisher = {Elsevier}
}

Rectification-based Knowledge Retention for Task Incremental Learning

Pratik Mazumder, Pravendra Singh, Piyush Rai, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Bib

@article{2024_TPAMI_mazumder_rectification,
  selected = true,
  title = {Rectification-based Knowledge Retention for Task Incremental Learning},
  author = {Mazumder, Pratik and Singh, Pravendra and Rai, Piyush and Namboodiri, Vinay P},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2024},
  publisher = {IEEE}
}

Protected Attribute Guided Representation Learning for Bias Mitigation in Limited Data

Pratik Mazumder and Pravendra Singh

Knowledge-Based Systems, 2022

Bib

@article{2022_KBS_mazumder_protected,
  selected = true,
  title = {Protected Attribute Guided Representation Learning for Bias Mitigation in Limited Data},
  author = {Mazumder, Pratik and Singh, Pravendra},
  journal = {Knowledge-Based Systems},
  volume = {238},
  pages = {107926},
  year = {2022},
  publisher = {Elsevier}
}

Hybrid Sample Synthesis-based Debiasing of Classifier in Limited Data Setting

Piyush Arora and Pratik Mazumder

In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

Bib

@inproceedings{2024_wacv_arora_hybrid,
  selected = true,
  title = {Hybrid Sample Synthesis-based Debiasing of Classifier in Limited Data Setting},
  author = {Arora, Piyush and Mazumder, Pratik},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year = {2024}
}

Few-Shot Lifelong Learning

Pratik Mazumder, Pravendra Singh, and Piyush Rai

In AAAI Conference on Artificial Intelligence (AAAI), 2021

Bib

@inproceedings{2021_aaai_mazumder_few,
  selected = true,
  title = {Few-Shot Lifelong Learning},
  author = {Mazumder, Pratik and Singh, Pravendra and Rai, Piyush},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021}
}

Calibrating cnns for lifelong learning

Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, and 2 more authors

In git Advances in Neural Information Processing Systems, 2020

Bib

@inproceedings{2020_neurips_singh_calibrating,
  selected = true,
  title = {Calibrating cnns for lifelong learning},
  author = {Singh, Pravendra and Verma, Vinay Kumar and Mazumder, Pratik and Carin, Lawrence and Rai, Piyush},
  booktitle = {git Advances in Neural Information Processing Systems},
  pages = {13702--13713},
  year = {2020}
}