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pro vyhledávání: '"A, Khaki"'
Autor:
Sajedi, Ahmad, Khaki, Samir, Liu, Lucy Z., Amjadian, Ehsan, Lawryshyn, Yuri A., Plataniotis, Konstantinos N.
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a model trained on the full dataset. Despite recent progress, existing data
Externí odkaz:
http://arxiv.org/abs/2411.12841
Autor:
Wang, Kai, Li, Zekai, Cheng, Zhi-Qi, Khaki, Samir, Sajedi, Ahmad, Vedantam, Ramakrishna, Plataniotis, Konstantinos N, Hauptmann, Alexander, You, Yang
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features)
Externí odkaz:
http://arxiv.org/abs/2410.17193
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
Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter fine
Externí odkaz:
http://arxiv.org/abs/2402.10038
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
Publikováno v:
Open Veterinary Journal, Vol 14, Iss 10, Pp 2618-2627 (2024)
Background: Trichomonas gallinae is a protozoan parasite responsible for canker in pigeons, a debilitating disease that causes significant economic losses. While metronidazole (MTZ) remains the primary treatment, the emergence of resistance is a grow
Externí odkaz:
https://doaj.org/article/113700f013d746e2b1cd3bb464790daf
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