Zobrazeno 1 - 10
of 418
pro vyhledávání: '"WANG Haohan"'
Publikováno v:
矿业科学学报, Vol 9, Iss 4, Pp 586-596 (2024)
This study investigates the fracture characteristics and damage mechanism of granite after treatment under different heating rates. The cracked straight-through Brazilian disc(CSTBD)specimen was used for high-temperature treatment with four heating r
Externí odkaz:
https://doaj.org/article/849b90da50df4cefa196fde2bff1308a
Adversarial training is the most effective method to obtain adversarial robustness for deep neural networks by directly involving adversarial samples in the training procedure. To obtain an accurate and robust model, the weighted-average method is ap
Externí odkaz:
http://arxiv.org/abs/2410.16579
Autor:
Wang, Peiran, Wang, Haohan
In this paper, we introduce DistDD, a novel approach within the federated learning framework that reduces the need for repetitive communication by distilling data directly on clients' devices. Unlike traditional federated learning that requires itera
Externí odkaz:
http://arxiv.org/abs/2410.08665
The complexity of text-embedded images presents a formidable challenge in machine learning given the need for multimodal understanding of the multiple aspects of expression conveyed in them. While previous research in multimodal analysis has primaril
Externí odkaz:
http://arxiv.org/abs/2409.14703
Autor:
Singh, Aditya, Wang, Haohan
As per recent studies, Self-supervised learning (SSL) does not readily extend to smaller architectures. One direction to mitigate this shortcoming while simultaneously training a smaller network without labels is to adopt unsupervised knowledge disti
Externí odkaz:
http://arxiv.org/abs/2409.13939
In the field of Alzheimer's disease diagnosis, segmentation and classification tasks are inherently interconnected. Sharing knowledge between models for these tasks can significantly improve training efficiency, particularly when training data is sca
Externí odkaz:
http://arxiv.org/abs/2409.07584
Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD) classification. However, understanding and interpreting these models remains challenging, which hinders the adoption of these models in clinical practice. Techniqu
Externí odkaz:
http://arxiv.org/abs/2409.04888
Autor:
Du, Zhenbang, Feng, Wei, Wang, Haohan, Li, Yaoyu, Wang, Jingsen, Li, Jian, Zhang, Zheng, Lv, Jingjing, Zhu, Xin, Jin, Junsheng, Shen, Junjie, Lin, Zhangang, Shao, Jingping
In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor co
Externí odkaz:
http://arxiv.org/abs/2408.00418
Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathological mechanism of AD remains not fully understood and may vary from pat
Externí odkaz:
http://arxiv.org/abs/2407.08546
The rapid evolution of artificial intelligence (AI) through developments in Large Language Models (LLMs) and Vision-Language Models (VLMs) has brought significant advancements across various technological domains. While these models enhance capabilit
Externí odkaz:
http://arxiv.org/abs/2407.01599