.png){kind=icon}
返回
Transfer learning with spinally shared layers
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2024.111908.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Transfer-learned models have achieved promising performance in numerous fields. However, high-performing transfer-learned models contain a large number of parameters. In this paper, we propose a transfer learning approach with parameter reduction and potential high performance. Although the high performance depends on the nature of the dataset, we ensure the parameter reduction. In the proposed SpinalNet shared parameters, all intermediate-split-incoming parameters except the first-intermediate-split contain a shared value. Therefore, the SpinalNet shared parameters network contains three parameter groups: (1) first input-split to intermediatesplit parameters, (2) shared intermediate-split-incoming parameters, and (3) intermediate-split-to-output-split parameters. The total number of parameters becomes lower than the SpinalNet and traditional fully connected layers due to parameter sharing. Besides the overall accuracy, this paper compares the precision, recall, and F1-score of each class as performance criteria. As a result, both parameter reduction and potential performance improvement become possible for the ResNet-type models, VGG-type traditional models, and Vision Transformers. We applied the proposed model to MNIST, STL-10, and COVID-19 datasets to validate our claims. We also provided a posterior plot of the sample from different models for medical practitioners to understand the uncertainty. Example model training scripts of the proposed model are also shared to GitHub.
Keyword:
Uncertainty
Transformer
SpinalNet
COVID
ResNet
VGG
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Alzheimer's Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes使用图像和基因表达数据和可解释的AI进行阿尔茨海默氏症患者分析以呈现相关基因
PREAI
分享收藏分享收藏Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications基于神经网络的不确定性量化: 方法与应用综述.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multi-criteria decision making under uncertainties in composite materials selection and designCOMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
分享收藏
查看更多内容