1 citations · 1 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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 diverse outputs remains underexplored. Existing divers…
Towards a Scalable Reference-Free Evaluation of Generative Models
Azim Ospanov, Jingwei Zhang, Mohammad Jalali +3
While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the una…
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li +1
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been…
Game of GANs: Game-Theoretical Models for Generative Adversarial Networks
Monireh Mohebbi Moghadam, Bahar Boroomand, Mohammad Jalali +4
Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to its ability to generate high-quality data of significant statistica…