To tackle the above problem, this paper proposes "Gated CNN" (short for "G-CNN") to introduce a "gate" structure to integrate multiple convolutional layers for object detection. Injected by multi-scale feature layers, a gate employs several filters to extract useful information and block noises...
参考代码:anantzoid/Language-Modeling-GatedCNN 之前语言模型的主流方法都是基于RNN,本篇论文提出了一种新颖的门控机制,结合CNN网络应用到语言模型。该网络包含多层,与经典语法形式相似,能分层次地分析输入,构建了增加粒度的句法树结构。与RNN逐个处理输入序列不同,CNN可以实现并行计算,大大加快训练速度。并且分层结构也...
The Mamba block extends the Gated CNN with an additional state space model (SSM). As will be conceptually discussed in Section 3, SSM is not necessary for image classification on ImageNet. To empirically verify this claim, we stack Gated CNN blocks to build a series of models named Mamba...
3.接下来是门限单元,A和sigmoid(B)进行element-wise的相乘,得到卷积后的结果。卷积单元和门限单元加起来形成一个卷积层(作者在具体实现里将卷积和门控单元放在一起,形成residual block)。 注意,LSTM中有input门和forget门两种,这两种缺一则会导致有些信息的缺失。而卷积中,作者经过实验,提出不需要forget gate。此外...
目前最先进的图像分割方法形成一个密集的图像表示,包括形状、颜色和纹理,都放入到了CNN网络中用来做识别,但是这可能不是最理想的方法,因为在语义分割任务中,我们需要更加关注物体的边界信息以得到物体的形状,而颜色和纹理信息相对来说就比较低级一点了。所以作者提出了两个流的方法,在常规流的基础之上增加了一个形状流...
图像分割目前存在的问题 现在的deep CNN能提取图像中很多feature map,比如图像的纹理、颜色、形状特征,但对于分割任务来说这并不是我们全部需要的,分割任务最理想的是根据边界和形状信息进行识别,如果信息流中包含了很多颜色、纹理可能会导致识别问题。 该网络提出的解决方法 针对上述问题,作者提出了双流CNN(two-stream...
Additionally, convolutions and GLUs can be wrapped in residual blocks adding the input of the block to the output for further mitigation of the degradation problem [44]. Since GCNNs have demonstrated state-of-the-art results in the field of NLP [43,45], we assume that they have the ...
由于residual network的强大能力,在真正的实现里,会把卷积单元和门限单元包在一个residual block里。 在最后的softmax层,普通的softmax会因为词表巨大而非常低效。因而选用adaptive softmax。adaptive softmax可以为高频词分配更多的空间而给低频次分配比较少的空间。
DeCNN utilizes a denoising module that effectively eliminates noise in the noisy channel matrix [19]. The DeCNN block has two layers: input and output, along with three denoising blocks. The three denoising blocks are connected in sequence, which enhances the effectiveness of the denoising ...
DeCNN utilizes a denoising module that effectively eliminates noise in the noisy channel matrix [19]. The DeCNN block has two layers: input and output, along with three denoising blocks. The three denoising blocks are connected in sequence, which enhances the effectiveness of the denoising ...