代码:GitHub - bdqnghi/treecaps: [AAAI 2021] - TreeCaps: Tree-based Capsule Network for Source Code Processing 二、模型 TBCNN层:使用树形卷积神经网络(Tree-based Convolutional Neural Network)提取抽象语法树(AST)中的节点特征。 PVC层(Primary
树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning,程序员大本营,技术文章内容聚合第一站。
Convolutional neural networkLoss functionApple tree disease is a main threat factor to apple quality and yield. This paper proposed an improved convolutional neural network model to classify apple tree diseases. It took the advantages of...
We used a custom deep learning framework developed in Python to segment the tree crown cover in PlanetScope images. This framework is an extension of the UNet architecture described in Brandt et al.16. The UNet is a convolutional neural network (CNN) architecture originally developed for medical...
Then trunk and branches of the tree that share the common appearance and features were segmented out using a convolutional neural network (SegNet) for the semantic segmentation. We achieved trunk and branch segmentation accuracy of 0.92 and 0.93 and the mean intersection-over-union (IoU) score of...
树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning 一、简介: 学习深度学习的人都知道,深度学习有一个严重的问题——“灾难性遗忘”,即一旦使用新的数据集去训练已有的模型,该模型将会失去对原数据集识别的能力。为解决这一问题,本文作者提出了树卷积神经网络Tree-CNN,通过先将物...
树卷积神经网络Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning 2018-04-17 08:32:39 看_这是一群菜鸟 阅读数 1906 收藏 更多 分类专栏: 论文解读
27 evaluated seven traditional machine learning models—KNN, SVM, LR, Convolutional Neural Network (CNN), Gradient Boost, XGBoost, and RF—on two datasets: the Cardiovascular Heart Disease Dataset and the Heart Disease Cleveland Dataset, each containing 1000 samples. When compared, the XGBoost model...
【转载】论文笔记系列-Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning 一、 引出主题¶ 深度学习领域一直存在一个比较严重的问题——“灾难性遗忘”,即一旦使用新的数据集去训练已有的模型,该模型将会失去对原数据集识别的能力。为解决这一问题,本文提出了树卷积神经网络,通过先将物体分为...
The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100. Introduction In recent years Deep Convolutional Neural Networks (DCNNs) have emerged as the leading ...