Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Khaki, Samir"'
Autor:
Li, Zekai, Guo, Ziyao, Zhao, Wangbo, Zhang, Tianle, Cheng, Zhi-Qi, Khaki, Samir, Zhang, Kaipeng, Sajedi, Ahmad, Plataniotis, Konstantinos N, Wang, Kai, You, Yang
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the t
Externí odkaz:
http://arxiv.org/abs/2408.03360
Autor:
Liu, Zhijian, Zhang, Zhuoyang, Khaki, Samir, Yang, Shang, Tang, Haotian, Xu, Chenfeng, Keutzer, Kurt, Han, Song
Semantic segmentation empowers numerous real-world applications, such as autonomous driving and augmented/mixed reality. These applications often operate on high-resolution images (e.g., 8 megapixels) to capture the fine details. However, this comes
Externí odkaz:
http://arxiv.org/abs/2407.19014
Autor:
Khaki, Samir, Sajedi, Ahmad, Wang, Kai, Liu, Lucy Z., Lawryshyn, Yuri A., Plataniotis, Konstantinos N.
Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real dataset. These approaches ultimately aim to attain test accuracy levels aki
Externí odkaz:
http://arxiv.org/abs/2405.01373
We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $\textit{withou
Externí odkaz:
http://arxiv.org/abs/2403.17921
Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture label depend
Externí odkaz:
http://arxiv.org/abs/2401.01448
Autor:
Sajedi, Ahmad, Khaki, Samir, Amjadian, Ehsan, Liu, Lucy Z., Lawryshyn, Yuri A., Plataniotis, Konstantinos N.
Publikováno v:
booktitle = Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) month = October year = 2023 pages = 17097-17107
Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that co
Externí odkaz:
http://arxiv.org/abs/2310.00093
Multilabel representation learning is recognized as a challenging problem that can be associated with either label dependencies between object categories or data-related issues such as the inherent imbalance of positive/negative samples. Recent advan
Externí odkaz:
http://arxiv.org/abs/2307.03967
Autor:
Khaki, Samir, Luo, Weihan
As the saying goes, sometimes less is more -- and when it comes to neural networks, that couldn't be more true. Enter pruning, the art of selectively trimming away unnecessary parts of a network to create a more streamlined, efficient architecture. I
Externí odkaz:
http://arxiv.org/abs/2306.04147
Autor:
Hosseini, Mahdi S., Bejnordi, Babak Ehteshami, Trinh, Vincent Quoc-Huy, Hasan, Danial, Li, Xingwen, Kim, Taehyo, Zhang, Haochen, Wu, Theodore, Chinniah, Kajanan, Maghsoudlou, Sina, Zhang, Ryan, Yang, Stephen, Zhu, Jiadai, Chan, Lyndon, Khaki, Samir, Buin, Andrei, Chaji, Fatemeh, Salehi, Ala, Nguyen, Bich Ngoc, Samaras, Dimitris, Plataniotis, Konstantinos N.
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digita
Externí odkaz:
http://arxiv.org/abs/2304.05482
Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs). While many methods explore NAS from a global search-space perspective, the employed optimization schemes typically req
Externí odkaz:
http://arxiv.org/abs/2110.06830