Data Science Seminar
Optimization methods for imposing Fairness in Computer Vision Models
Vishnu Lokhande
Optimization methods for imposing Fairness in Computer Vision Models
| When | Friday, September 18, 2020, 11:50 AM – 1:10 PM (MT) |
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Abstract
In this talk, we will study a mechanism to impose fairness in computer vision models concurrently while training the model and informed by standard fairness measures. While existing fairness based approaches in vision have largely relied on training adversarial modules together with the primary classification/regression task, in an effort to remove the influence of the protected attribute or variable, we will discuss how ideas based on well-known optimization concepts can provide a simpler alternative. In our proposed scheme, imposing fairness just requires specifying the protected attribute and utilizing our optimization routine. We will discuss experiments, that are interpretable, demonstrating that several fairness measures from the literature can be reliably imposed on standard vision tasks. We will also discuss technical analysis on the convergence guarantees of the said optimization routine.
Speaker
Vishnu Lokhande
University of Wisconsin-Madison
Vishnu Lokhande is a fourth year PhD student in Computer Sciences at the University of Wisconsin-Madison. He is currently completing his research internship at Microsoft Research in the Interactive Media Group. His research interests include Algorithmic Fairness, Semi-Supervised Learning, Constrained and Stochastic Optimization problems. Prior to his graduate studies, he received his bachelor’s in Electrical Engineering at the Indian Institute of Technology Kanpur.
Tags: computer vision fairness & ethics machine learning optimization
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