https://github.com/ksasi/masters-thesis

Repository for my master's thesis work
https://github.com/ksasi/masters-thesis

Last synced: 22 days ago
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Repository for my master's thesis work

• Host: GitHub
• URL: https://github.com/ksasi/masters-thesis
• Owner: ksasi
• License: apache-2.0
• Created: 2023-01-12T11:09:54.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-25T03:54:13.000Z (almost 2 years ago)
• Last Synced: 2024-04-20T00:49:25.983Z (7 months ago)
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# [Bias and Fairness in Low Resolution Image Recognition](https://ksasi.github.io/pdfs/MT19AIE308-Report.pdf)

This repository contains details related to my Masters Thesis - ***"[Bias and Fairness in Low Resolution Image Recognition](https://ksasi.github.io/pdfs/MT19AIE308-slide.pdf)"*** completed under the guidance of [Dr. Mayank Vatsa](http://home.iitj.ac.in/~mvatsa/) and [Dr. Richa Singh](http://home.iitj.ac.in/~richa/)

## Abstract:

      Recent image recognition algorithms showed great performance, which was possible due to advancements in deep learning methods. These methods find application in variety of scenarios such as recognising

species, surveillance, identifying missing persons, identifying objects from drones during floods etc. However, current methods assume the availability of images of very high resolution. Obtaining images of high resolution is not always possible due to large distance of the object and inherent limitations in the

acquisition device such as 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.

      Recent algorithms in the literature showed decent improvement in performance. However, bias and

fairness was not taken into consideration in the current algorithms specially 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 had also showed that existing generative models specifically GANs are prone to bias and fairness issues

and can cause cause disparate impact. Lastly we proposed methods and techniques to debiase existing

generative models. We hope these techniques can be used to develop fair algorithms for low resolution

image recognition.

## Code:

* Low Resolution face recognition - [Github Link](https://github.com/ksasi/face-recognition)

* Bias and Fairness in GANs - [Github Link](https://github.com/ksasi/fairDL)

* FairDistillation in GANs - [Github Link](https://github.com/ksasi/fair-distill)

## How to cite this repository

If you used this repository in your work, please cite as below:

```

@article{kotti2023thesis,

  author = {Sasikanth, Kotti},

  title = {Bias and Fairness in Low Resolution Image Recognition},

  year = {2023},

  url = {https://ksasi.github.io/pdfs/MT19AIE308-Report.pdf}

}

```