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Ibtihel Amara

2 accepted papers

2025

Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts

ICCV 2025poster

Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate promising results in controlled settings, their robustness in real-world applications and suitability for deployment…

Cited by 0SourcePDFScholar
2025

What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models

ICLR 2025poster

Deep Generative Models are frequently used to learn continuous representations of complex data distributions by training on a finite number of samples. For any generative model, including pre-trained foundation models with Diffusion or Transformer architectures, generation performance can significan…