Bias and Fairness in Low Resolution(Very) Image Recognition (Record no. 16584)

MARC details
000 -LEADER
fixed length control field 02203nam a22001817a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number K877B
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Kotti, Sasikanth
245 ## - TITLE STATEMENT
Title Bias and Fairness in Low Resolution(Very) Image Recognition
Statement of responsibility, etc by Sasikanth Kotti
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages x,33p.
Other physical details HB
500 ## - GENERAL NOTE
General note Recent image recognition algorithms showed great performance, which was possible due to advancements in deep learning methods. These methods find application in a variety of scenarios such as recognizing species, surveillance, identifying missing persons, and identifying objects from drones during floods. However, current methods assume the availability of images of very high resolution. Obtaining images of high resolution is not always possible due to the large distance of the object and inherent limitations in the acquisition device, such as the camera. Hence, it is important to develop robust algorithms that can also work with low-resolution and very low-resolution images. It is also essential that these algorithms are not biased and are fair to different sub-groups.<br/><br/>Recent algorithms in the literature showed decent improvement in performance. However, bias and fairness were not taken into consideration in the current algorithms, especially in components that use generative models. Hence, there is enormous scope to further improve the performance of image recognition with low resolution along with developing fair algorithms. In this work, we attempted to cover existing literature. We showed experimentally that this problem needs further research for improved performance. We also showed that existing generative models, specifically GANs, are prone to bias and fairness issues and can cause disparate impact. Lastly, we proposed methods and techniques to debias existing generative models. We hope these techniques can be used to develop fair algorithms for low-resolution image recognition.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term Generative Models
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Vatsa, Mayank
Personal name Singh. Richa
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
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        Theses S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 006.3 K877B TM00520 2024-06-29 Thesis