Yara Bahram — accepted AI-conference papers

2026

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models

Yara Bahram, Mohammadhadi Shateri, Eric Granger

AAAI 2026technical

Transfer learning of diffusion models to smaller target domains is challenging, as naively fine-tuning the model often results in poor generalization. Test-time guidance methods help mitigate this by offering controllable improvements in image fidelity through a trade-off with sample diversity. Howe

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2026

Uni-DAD: Unified Distillation and Adaptation of Diffusion Models for Few-step Few-shot Image Generation

Yara Bahram, Mélodie Desbos, Mohammadhadi Shateri, Eric Granger

CVPR 2026

Diffusion models (DMs) produce high-quality images, yet their sampling remains costly when adapted to new domains. Distilled DMs are faster but typically remain confined within their teacher's domain. Thus, fast and high-quality generation for novel domains relies on two-stage pipelines: Adapt-then-

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