Search Results for author:
Found 4 papers, 2 papers with code
The Bias Amplification Paradox in Text-to-Image Generation
Bias amplification is a phenomenon in which models exacerbate biases or stereotypes present in the training data.
Quantifying Social Biases Using Templates is Unreliable
Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases.
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.
A Seed-Augment-Train Framework for Universal Digit Classification
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
