10 papers
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali +1
Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the character…
Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
Mohammad Jalali, Azim Ospanov, Amin Gohari +1
Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored. Existing diversity metr…
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
Youqi Wu, Mohammad Jalali, Farzan Farnia
Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are typically compared through dow…
Exposing Diversity Bias in Deep Generative Models: Statistical Origins and Correction of Diversity Error
Farzan Farnia, Mohammad Jalali, Azim Ospanov
Deep generative models have achieved great success in producing high-quality samples, making them a central tool across machine learning applications. Beyond sample quality, an imp…
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…