2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu +3
Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We pr…
From Pixels to Prose: A Large Dataset of Dense Image Captions
Vasu Singla, Kaiyu Yue, Sukriti Paul +7
Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To…
Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models
Alireza Ganjdanesh, Reza Shirkavand, Shangqian Gao +1
Text-to-image (T2I) diffusion models have demonstrated impressive image generation capabilities. Still, their computational intensity prohibits resource-constrained organizations f…
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment
Alireza Ganjdanesh, Shangqian Gao, Heng Huang
Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained de…