7 papers
Diff-CA: Separating Common and Salient Factors with Diffusion Models
Michaël Soumm, Alexandre Fournier Montgieux, Yunlong He +2
Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based…
Quantifying User Coherence: A Unified Framework for Analyzing Recommender Systems Across Domains
Michaël Soumm, Alexandre Fournier-Montgieux, Adrian Popescu +1
The performance of Recommender Systems (RS) varies significantly across users, yet the underlying reasons for this variance remain poorly understood. This paper introduces a unifie…
SAGA: Learning Signal-Aligned Distributions for Improved Text-to-Image Generation
Paul Grimal, Michaël Soumm, Hervé Le Borgne +2
State-of-the-art text-to-image models produce visually impressive results but often struggle with precise alignment to text prompts, leading to missing critical elements or uninten…
Learning Common and Salient Generative Factors Between Two Image Datasets
Yunlong He, Gwilherm Lesné, Ziqian Liu +2
Recent advancements in image synthesis have enabled high-quality image generation and manipulation. Most works focus on: 1) conditional manipulation, where an image is modified con…
Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
Alexandre Fournier-Montgieux, Michael Soumm, Adrian Popescu +2
Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However, ethical, legal, and technic…
Toward Fairer Face Recognition Datasets
Alexandre Fournier-Montgieux, Michael Soumm, Adrian Popescu +2
Face recognition and verification are two computer vision tasks whose performance has progressed with the introduction of deep representations. However, ethical, legal, and technic…