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Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. \revise{Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions
Externí odkaz:
http://arxiv.org/abs/2411.06318
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
Image inpainting, or image completion, is a crucial task in computer vision that aims to restore missing or damaged regions of images with semantically coherent content. This technique requires a precise balance of local texture replication and globa
Externí odkaz:
http://arxiv.org/abs/2407.16126
Publikováno v:
Infection and Drug Resistance, Vol Volume 13, Pp 1429-1437 (2020)
Mehrdad Halaji,1,2 Shahrzad Shahidi,3 Abdolamir Atapour,3 Behrooz Ataei,4 Awat Feizi,5 Seyed Asghar Havaei1,4 1Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; 2Nosocomial Infection Research Cente
Externí odkaz:
https://doaj.org/article/322668364ebd40818e06ee409b8a42bf
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of r
Externí odkaz:
http://arxiv.org/abs/2404.02189
Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges
Externí odkaz:
http://arxiv.org/abs/2403.19897
Federated Learning (FL) has emerged as a prominent alternative to the traditional centralized learning approach. Generally speaking, FL is a decentralized approach that allows for collaborative training of Machine Learning (ML) models across multiple
Externí odkaz:
http://arxiv.org/abs/2403.11892
Existing image inpainting methods leverage convolution-based downsampling approaches to reduce spatial dimensions. This may result in information loss from corrupted images where the available information is inherently sparse, especially for the scen
Externí odkaz:
http://arxiv.org/abs/2402.14185
Autor:
Atapour, Kawa, Seyedmohammadi, S. Jamal, Abouei, Jamshid, Mohammadi, Arash, Plataniotis, Konstantinos N.
This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework. The model-drift issue, arising from the noniid nature of client data, often results in suboptimal personalization of a globa
Externí odkaz:
http://arxiv.org/abs/2402.10846
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Vasoactive intestinal polypeptide (VIP) is known to be present in a subclass of cortical interneurons. Here, using three different antibodies, we demonstrate that VIP is also present in the giant layer 5 pyramidal (Betz) neurons which are ch
Externí odkaz:
https://doaj.org/article/2c3d726452134f7281b67a917f702a07