Anurag Singh

I am a second year PhD student at Rational Intelligence Group in CISPA Helmholtz Center for Information Security, Saarbr├╝cken, Germany, where I am advised by Dr. Krikamol Muandet. I am broadly interested in the area of Trustworthy Machine Learning.
Previously, I obtained my Masters in Computer Science at the TU Munich where I was advised by Prof. Matthias Neissner and Prof. Debarghya Ghoshdastidar. Even before, I completed my BEng. from Netaji Subhas Institute of Technology, University of Delhi.

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Publications
Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics
Anurag Singh*, Naren Doraiswamy*, Sawa Takamuku, Megh Bhalerao, Titir Dutta
Soma Biswas, Aditya Chepuri
CVPR Workshop Learning with Limited and Imperfect Data, 2021
Talk
Adaptive Margin Diversity Regularizer for handling Data Imbalance in Zero-Shot SBIR
Titir Dutta, Anurag Singh, Soma Biswas
ECCV, 2020 (Spotlight Presentation ~5% acceptance rate)

We analyze the effect of class-imbalance on generalization to unseen classes for the ZS-SBIR. The work then proposes a novel regularizer termed AMDReg, which can seamlessly be used with several ZS-SBIR methods to improve their performance.

StyleGuide: Zero-Shot Sketch-based Image Retrieval Using Style-Guided Image Generation
Titir Dutta, Anurag Singh, Soma Biswas
IEEE Transactions on Multimedia, 2020

A style-guided image generation during retrieval to eliminate the effect of domain difference and intra-class variations.

Image Corpus Representative Summarization
Anurag Singh, Lakshay Virmani, AV Subramanyam
IEEE International Conference on Multimedia Big Data, 2019
(Honourable Mention Award, Best Paper Nomination)
Poster

This work is part of my undergraduate thesis which focused on developing end to end deep learning architecture for problem of Image Collection Summarization. We introduced a task-specific loss to generate the summary related to a given task. We also proposed analysis of goodness of summary by training a classifier on it and comparing its performance with entire dataset.

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