Zobrazeno 1 - 10
of 783
pro vyhledávání: '"Plataniotis, Konstantinos"'
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:
Mansoori, Mobina, Shahabodini, Sajjad, Abouei, Jamshid, Plataniotis, Konstantinos N., Mohammadi, Arash
Early diagnosis and treatment of polyps during colonoscopy are essential for reducing the incidence and mortality of Colorectal Cancer (CRC). However, the variability in polyp characteristics and the presence of artifacts in colonoscopy images and vi
Externí odkaz:
http://arxiv.org/abs/2409.09484
Autor:
Atapour, S. Kawa, SeyedMohammadi, S. Jamal, Sheikholeslami, S. Mohammad, Abouei, Jamshid, Plataniotis, Konstantinos N., Mohammadi, Arash
Recently pre-trained Foundation Models (FMs) have been combined with Federated Learning (FL) to improve training of downstream tasks while preserving privacy. However, deploying FMs over edge networks with resource-constrained Internet of Things (IoT
Externí odkaz:
http://arxiv.org/abs/2409.09273
Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classification, involve computationally intensive processes, and fail to capture
Externí odkaz:
http://arxiv.org/abs/2408.16871
Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A common approach in DRL involves learning the quantiles of loss distributi
Externí odkaz:
http://arxiv.org/abs/2408.12446
Autor:
Mansoori, Mobina, Shahabodini, Sajjad, Abouei, Jamshid, Plataniotis, Konstantinos N., Mohammadi, Arash
Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a gene
Externí odkaz:
http://arxiv.org/abs/2408.05892
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:
Wang, Ziqiang, Chi, Zhixiang, Wu, Yanan, Gu, Li, Liu, Zhi, Plataniotis, Konstantinos, Wang, Yang
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predominantly optimize the model for each incoming test data batch using self-t
Externí odkaz:
http://arxiv.org/abs/2407.12128
Autor:
Liang, Hanwen, Yin, Yuyang, Xu, Dejia, Liang, Hanxue, Wang, Zhangyang, Plataniotis, Konstantinos N., Zhao, Yao, Wei, Yunchao
The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rely on multiple image or video diffusion models, utilizing score distill
Externí odkaz:
http://arxiv.org/abs/2405.16645
Autor:
Chi, Zhixiang, Gu, Li, Zhong, Tao, Liu, Huan, Yu, Yuanhao, Plataniotis, Konstantinos N, Wang, Yang
In this paper, we aim to adapt a model at test-time using a few unlabeled data to address distribution shifts. To tackle the challenges of extracting domain knowledge from a limited amount of data, it is crucial to utilize correlated information from
Externí odkaz:
http://arxiv.org/abs/2405.02797