1 citations · 1 across the 5 of their papers we have counts for
5 papers
Understanding and evaluating computer vision models through the lens of counterfactuals
Pushkar Shukla
Counterfactual reasoning -- the practice of asking ``what if'' by varying inputs and observing changes in model behavior -- has become central to interpretable and fair AI. This th…
Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension - such…
BiasConnect: Investigating Bias Interactions in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimensio…
Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement
Pushkar Shukla, Dhruv Srikanth, Lee Cohen +1
We propose a novel approach to mitigate biases in computer vision models by utilizing counterfactual generation and fine-tuning. While counterfactuals have been used to analyze and…
TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models
Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt +4
Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery. At the same time, thes…