3 papers
cs.LG2026
Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
Ivan Luiz De Moura Matos, Abdel Djalil Sad Saoud, Ekaterina Iakovleva +2
The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulati…
cs.LG2025
How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Nathan Roos, Ekaterina Iakovleva, Ani Gjergji +2
Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains…
cs.CV2024
Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing
Ekaterina Iakovleva, Fabio Pizzati, Philip Torr +1
Text-based editing diffusion models exhibit limited performance when the user's input instruction is ambiguous. To solve this problem, we propose (SANE)…