Dilated Shuffle CNN
时间: 2023-12-04 07:06:49 浏览: 19
Dilated Shuffle CNN是一种卷积神经网络结构,它结合了空洞卷积(dilated convolution)和ShuffleNet的思想,旨在提高模型的精度和计算效率。其中,空洞卷积可以增加感受野,提高特征提取能力; ShuffleNet则可以减少参数量和计算量,提高计算效率。Dilated Shuffle CNN在图像分类、目标检测等任务中取得了不错的效果。
相关问题
dilated resnet
Dilated Residual Networks(DRN)是在残差网络的基础上加入了膨胀卷积(dilated convolution)的一种神经网络结构。膨胀卷积可以增大卷积核的感受野,使得网络可以捕捉更广阔范围的上下文信息,从而提高了网络的感知能力。在DRN结构中,为了避免gridding现象的产生,需要去除最后两层的残差连接。此外,DRN结构在保持特征图尺寸不再减小的同时,通过增加卷积核的膨胀系数来提高网络的感受野。
stacked dilated convolutions
Stacked dilated convolutions refer to a type of convolutional neural network (CNN) architecture where multiple dilated convolutions are used in a series (or "stack") to extract features from the input data. Dilated convolutions are a type of convolution where the filter is applied to the input with a certain spacing between the filter elements, allowing the network to have a larger receptive field without increasing the number of parameters. By stacking multiple dilated convolutions, the network can learn more complex features and patterns from the input data, which can improve the accuracy of the model. This architecture has been used in various applications, including image segmentation, speech recognition, and natural language processing.