Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Xiao, Jiayu"'
Autor:
Wang, Zihan, Xiao, Jiayu, Li, Mengxiang, He, Zhongjiang, Li, Yongxiang, Wang, Chao, Song, Shuangyong
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch. A major challenge in continual learning of language model is catastrophic f
Externí odkaz:
http://arxiv.org/abs/2403.10894
Diffusion-based text-to-image personalization have achieved great success in generating subjects specified by users among various contexts. Even though, existing finetuning-based methods still suffer from model overfitting, which greatly harms the ge
Externí odkaz:
http://arxiv.org/abs/2401.16762
Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout instruction. In th
Externí odkaz:
http://arxiv.org/abs/2310.08872
Autor:
Zhang Yingjie, Md Miftahul Mithu, Md Ariful Haque, Xiao Jiayu, Lu Jipeng, Chen Shuai, Wu Tong
Publikováno v:
Journal of Orthopaedic Surgery and Research, Vol 19, Iss 1, Pp 1-8 (2024)
Abstract Plantar fascia (PF) is the commonest causes of foot pain in the adult population. Several surgical treatments are available to treat PF. This study was aimed to investigate the clinical efficacy of three different treatments for plantar fasc
Externí odkaz:
https://doaj.org/article/471af1fade7b4126b0043c4bbcc6289c
Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few
Externí odkaz:
http://arxiv.org/abs/2203.04121
The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification. We propose a novel loss function, termed as CAM-loss, to constrain the embedded feature maps with
Externí odkaz:
http://arxiv.org/abs/2109.01359
Publikováno v:
In Applied Thermal Engineering 1 March 2024 240
Autor:
Chen, Yuhua, Ruan, Dan, Xiao, Jiayu, Wang, Lixia, Sun, Bin, Saouaf, Rola, Yang, Wensha, Li, Debiao, Fan, Zhaoyang
Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) framework based on 2D U-net
Externí odkaz:
http://arxiv.org/abs/1912.11000
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Akademický článek
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