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cs.LG2024
Who's the (Multi-)Fairest of Them All: Rethinking Interpolation-Based Data Augmentation Through the Lens of Multicalibration
Karina Halevy, Karly Hou, Charumathi Badrinath
Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on…
cs.LG2024
All Roads Lead to Rome? Exploring Representational Similarities Between Latent Spaces of Generative Image Models
Charumathi Badrinath, Usha Bhalla, Alex Oesterling +2
Do different generative image models secretly learn similar underlying representations? We investigate this by measuring the latent space similarity of four different models: VAEs,…