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researcher

Matthew Turk

4 papers hereh-index 267 citations4 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Matthew Turk — 6 papers, h 1
  • Matthew Turk — 3 papers
  • Matthew Turk — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232025
most citedTIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…

cs.CV2023★ 1 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.