Dilated Shuffle CNN

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 3D CNN

Dilated Convolutional Neural Networks (Dilated CNNs), also known as atrous convolutions or convolution with holes, are a type of deep learning architecture that extends standard convolutional layers by introducing gaps between the filter weights. These gaps allow the network to maintain its receptive field while increasing the spatial resolution of the feature maps without reducing the number of filters or requiring more computation.

In a dilated 3D CNN[^4], each kernel moves through the input volume not only in the depth but also along the height and width dimensions, with dilation factors applied at regular intervals. This enables the model to capture long-range dependencies within volumetric data, such as medical images or videos, without losing spatial details due to pooling operations.

For example, consider a 3x3x3 kernel with a dilation rate of 2:

from keras.layers import Conv3D, MaxPooling3D

# Dilated Convolution
dilated_conv = Conv3D(filters=64, kernel_size=(3, 3, 3), dilation_rate=(2, 2, 2), activation='relu')

# Without dilation, the kernel would touch every pixel; with dilation, it skips one.
# Output size will be same or slightly larger than input size depending on padding.

# Optional downsampling using MaxPooling3D can follow for further context aggregation
max_pool = MaxPooling3D(pool_size=(2, 2, 2))

# The combined layer sequence captures both local and distant features in the 3D volume.

dilated convolution

Dilated Convolution（膨胀卷积）是在标准卷积的基础上注入空洞，以增加感受野（reception field）的一种卷积操作。它通过在卷积核中引入间隔（dilation rate），使得卷积核在输入特征图上取样的位置有间隔，从而扩大了卷积核的感受野。与标准卷积相比，膨胀卷积可以在不增加参数和计算量的情况下，增加特征图的有效感受野。通过增大膨胀率，膨胀卷积可以进一步扩大感受野，从而捕捉到更远距离的上下文信息。