Domain Adversarial Neural Network(DANN): 原文:Domain 迁移学习概述(Transfer Learning) 能,即使目标task经过了大量的调整依然如此。 DANN (Domain-Adversarial Neural Network) 这篇paper将近两年流行的对抗网络思想引入到迁移学习中,从而提出了...领域间的概率分布失配问题。 迁移学习的形式定义及一种分类方式 迁移...
The proposed architecture basically consists of two parts, a resonator-bank digital filter (RBDF) and a neural network (NN). The RBDF provides effective implementation of orthogonal transforms recursively. The NN is utilized to perform a nonlinear mapping between its input vector U(n) and the ...
In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context featu...
a common theme of several neural network models is of the "self-organization" type as with the self-organizing maps (SOM) o=-=f Kohonen [13, 14, 15]-=-, the adaptive resonance theory (ART) models of Carpenter [16, 17, 18... T Kohonen - 《Advanced Neural Computers》 被引量: 49...
Zhu Y, Qiu Y, Wu Q, Wang FL, Rao Y (2023) Topic driven adaptive network for cross-domain sentiment classification. Inf Process Manag 60(2):103230. https://doi.org/10.1016/j.ipm.2022.103230 Article Google Scholar Wang W, Pan SJ (2018) Recursive neural structural correspondence network ...
Network initialization.与ANN相比,SRNN需要初始化权重和脉冲神经元的超参数(即神经元类型、时间常数、阈值、起始电位)。我们使用补充表1中给出的每层特定参数随机初始化时间常数,遵循严格的正态分布(μ, σ)。对于所有神经元,膜电位的起始值用在范围内均匀分布的随机值初始化 [0,θ]。网络的偏置权重初始化为零,...
Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network Mining user consumption intention from social media using domain adaptive convolutional neural network[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI ...
Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images re...
leading to deterioration of sequence characterization if all parameters are updated. On the other hand, using such a huge neural network to fit a relatively small amount of training data will undoubtedly lead to overfitting and thus learning redundant noise, which can also lead to worsening ...
(\mathrm{ConvA}\)(i.e., local consistency network). It adopts the GCN model proposed by Kipf16. We briefly describe\(ConvA\)as a deep feedforward neural network. Input a feature set X and an adjacency matrix A, and output the embedding Z of the i-th hidden layer of the network as...