5 papers
PromptSplit: Revealing Prompt-Level Disagreement in Generative Models
Mehdi Lotfian, Mohammad Jalali, Farzan Farnia
Prompt-guided generative AI models have rapidly expanded across vision and language domains, producing realistic and diverse outputs from textual inputs. The growing variety of suc…
On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations
Haoyu Lei, Mohammad Jalali, Chin Wa Lau +1
Recent advances in deep learning have led to AI-based error correction decoders that report empirical performance improvements over traditional belief-propagation (BP) decoding on…
SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
Mohammad Jalali, Haoyu Lei, Amin Gohari +1
Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samp…
Towards an Explainable Comparison and Alignment of Feature Embeddings
Mohammad Jalali, Bahar Dibaei Nia, Farzan Farnia
While several feature embedding models have been developed in the literature, comparisons of these embeddings have largely focused on their numerical performance in classification-…
Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings
Azim Ospanov, Mohammad Jalali, Farzan Farnia
The use of CLIP embeddings to assess the fidelity of samples produced by text-to-image generative models has been extensively explored in the literature. While the widely adopted C…