142 citations · 190 across the 20 of their papers we have counts for
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Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Markos Georgopoulos, Grigorios Chrysos, Maja Pantic +1
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images. However, empirical studies have s…
Enhancing Facial Data Diversity with Style-based Face Aging
Markos Georgopoulos, James Oldfield, Mihalis A. Nicolaou +2
A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as…
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
Markos Georgopoulos, Yannis Panagakis, Maja Pantic
Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias…