2.1.1 空洞卷积 Temporal dilated convolutional layer: 利用参数为 2J 3d1 1024的卷积层提取信息, 其中J = 17, 即是输入的2D Pose的关键点个数, 也是输出的3D Pose的关键点个数. 3d1代表卷积核的kernel size = 3, d代表空洞卷积, 1为dilation factor, 当d = 1时和常规的31卷积核一样,当d=2时, 中...
长依赖关系建模:传统的卷积操作在局部感受野内工作,导致原始的TCN可能对较长的序列数据难以捕捉长期依赖关系。然而,通过使用一些技术,如深层TCN和膨胀卷积(dilated convolutions),TCN可以扩大感受野并增加信息传递的距离,从而更有效地捕捉和建模长期依赖关系。 多尺度信息提取:TCN可以通过堆叠多个卷积层来提取不同尺度的特征。
TCN基本结构 时域卷积网络(Temporal Convolutional Network,TCN)由Shaojie Bai et al.在2018年提出的,可以用于时序数据处理,详细内容请看论文。 1.因果卷积(Causal Convolution) 因果卷积如上图所示。对于上一层t时刻的值,只依
First, the model utilizes dilated convolution with enlarged receptive fields to enhance global feature extraction in time series. Secondly, to effectively capture the long-term dependency and to further extract multiscale features that represent different operating conditions, the model is augmented with ...
IGR-TCN reduces the computational complexity by using a convolutional structure, the temporal convolution layer uses dilated convolution, and causal convolution to optimize the long-term prediction capability. The graph representation proposed in this work improves the existing spatial-temporal correlation ...
IEEEAbstract—In this paper, we propose a novel SpatioTemporalconvolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decompositionof 3D convolution and the 3D spatiotemporal dilated denseconvolution to alleviate the rapid growth of the model size cau...
Fig. 3. 1D dilated Convolutional layer computation: An explicit illustration of mappings from layer “l − 1” to layer “l” where “kl” kernels are applied. 3.2. Residual and skip connections Residual connections, originally introduced by He et al. (2016), were devised to tackle a comm...
(L. Zhang and M. Wang, “Multi-Scale TCN: Exploring Better Temporal DNN Model for Causal Speech Enhancement,” in Proc. Interspeech 2020, 2020, pp. 2672–2676. )它使用TCN来学习噪声和清洁语音信号之间的对数功率谱(LPS)的映射函数,并在TCN中采用从噪声输入到每个剩余块的跳过连接。TCN的性能优势...
这就是 TCN 的基本思想。 2018年 Google、Facebook 相继发表了研究成果,其中一篇叙述比较全面的论文是 "An Empirical Evaluation of Generic Convolutional and Recurrent Networks"。业界将这一新架构命名为时间卷积网络(Temporal Convolutional Network,TCN)。TCN 模型以 CNN 模型为基础,并做了如下改进:...
1.1 model 整体架构 由于原 paper 所写 model 有两个,分别是用 ChebNet 和 GCN 的 Graph Convolution 的 STGCN,原作者定义为 STGCN(Cheb) 和 STGCN(1^{st}),我在代码中定义为 STGCN(ChebGraphConv) 和 STGCN(GraphConv)。 Causal Convolution