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pro vyhledávání: '"Mittal, Surbhi"'
This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their per
Externí odkaz:
http://arxiv.org/abs/2408.00283
Autor:
Mittal, Surbhi, Thakral, Kartik, Singh, Richa, Vatsa, Mayank, Glaser, Tamar, Ferrer, Cristian Canton, Hassner, Tal
Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI tec
Externí odkaz:
http://arxiv.org/abs/2310.15848
The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face d
Externí odkaz:
http://arxiv.org/abs/2211.03588
Autor:
Narayan, Kartik, Agarwal, Harsh, Thakral, Kartik, Mittal, Surbhi, Vatsa, Mayank, Singh, Richa
Deepfake refers to tailored and synthetically generated videos which are now prevalent and spreading on a large scale, threatening the trustworthiness of the information available online. While existing datasets contain different kinds of deepfakes w
Externí odkaz:
http://arxiv.org/abs/2209.09111
Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin
Externí odkaz:
http://arxiv.org/abs/2112.06522
Identifying and mitigating bias in deep learning algorithms has gained significant popularity in the past few years due to its impact on the society. Researchers argue that models trained on balanced datasets with good representation provide equal an
Externí odkaz:
http://arxiv.org/abs/2108.06581
Autor:
Malhotra, Aakarsh, Mittal, Surbhi, Majumdar, Puspita, Chhabra, Saheb, Thakral, Kartik, Vatsa, Mayank, Singh, Richa, Chaudhury, Santanu, Pudrod, Ashwin, Agrawal, Anjali
With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testin
Externí odkaz:
http://arxiv.org/abs/2008.03205
Akademický článek
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Autor:
Malhotra, Aakarsh, Mittal, Surbhi, Majumdar, Puspita, Chhabra, Saheb, Thakral, Kartik, Vatsa, Mayank, Singh, Richa, Chaudhury, Santanu, Pudrod, Ashwin, Agrawal, Anjali
Publikováno v:
In Pattern Recognition February 2022 122
Publikováno v:
Saudi Journal of Oral Sciences; May-Aug2024, Vol. 11 Issue 2, p146-150, 5p