70 citations · 193 across the 12 of their papers we have counts for
3 papers · 1 filter
Does Prompt Design Impact Quality of Data Imputation by LLMs?
Shreenidhi Srinivasan, Lydia Manikonda
Generating realistic synthetic tabular data presents a critical challenge in machine learning. It adds another layer of complexity when this data contain class imbalance problems.…
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses
Niharika Jain, Alberto Olmo, Sailik Sengupta +2
In this paper, we show that popular Generative Adversarial Networks (GANs) exacerbate biases along the axes of gender and skin tone when given a skewed distribution of face-shots.…
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases
Niharika Jain, Lydia Manikonda, Alberto Olmo Hernandez +2
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners c…