翻译：其中U-Net[63]在ResNet残差结构的基础上开发了将编码器与解码器之间连接起来的远跳连接，巧妙的桥接了编码端与解码端的语义鸿沟，在一段时期内，成为了最常用的语义分割模型。

Translation: U-Net[63] developed far-skip connections that link the encoder and decoder based on the residual structure of ResNet, cleverly bridging the semantic gap between the encoding and decoding ends, becoming the most commonly used semantic segmentation model for a period of time.

相关问题

Deep Residual U-Net

Deep Residual U-Net是一种基于U-Net和ResNet的图像分割网络。它采用了U-Net的编码器-解码器结构，同时在每个解码器块中使用了ResNet的残差块（DR块）来提高特征提取能力。DR块通过引入SE（Squeeze-and-Excitation）机制来增强编码器的全局特征提取能力，同时使用1×1卷积来改变特征图的维度，以确保3×3卷积滤波器不受前一层的影响。此外，为了避免网络太深的影响，在两组Conv 1×1-Conv 3×3操作之间引入了一条捷径，允许网络跳过可能导致性能下降的层，并将原始特征转移到更深的层。Deep Residual U-Net在多个图像分割任务中都取得了优秀的性能。

以下是Deep Residual U-Net的编码器-解码器结构示意图：

import torch.nn as nn

class DRBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DRBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(out_channels, out_channels // 16, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels // 16, out_channels, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.se(out) * out
        out += identity
        out = self.relu(out)
        return out

class DRUNet(nn.Module):
    def __init__(self, in_channels, out_channels, init_features=32):
        super(DRUNet, self).__init__()
        features = init_features
        self.encoder1 = nn.Sequential(
            nn.Conv2d(in_channels, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder2 = nn.Sequential(
            DRBlock(features, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 2, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder3 = nn.Sequential(
            DRBlock(features * 2, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 4, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.encoder4 = nn.Sequential(
            DRBlock(features * 4, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 8, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottleneck = nn.Sequential(
            DRBlock(features * 8, features * 16),
            nn.Conv2d(features * 16, features * 16, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 16, features * 16),
            nn.Conv2d(features * 16, features * 16, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)
        self.decoder4 = nn.Sequential(
            DRBlock(features * 16, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 8, features * 8),
            nn.Conv2d(features * 8, features * 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)
        self.decoder3 = nn.Sequential(
            DRBlock(features * 8, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 4, features * 4),
            nn.Conv2d(features * 4, features * 4, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)
        self.decoder2 = nn.Sequential(
            DRBlock(features * 4, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features * 2, features * 2),
            nn.Conv2d(features * 2, features * 2, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )
        self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)
        self.decoder1 = nn.Sequential(
            DRBlock(features * 2, features),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            DRBlock(features, features),
            nn.Conv2d(features, features, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

        self.conv = nn.Conv2d(features, out_channels, kernel_size=1)

    def forward(self, x):
        enc1 = self.encoder1(x)
        enc2 = self.encoder2(self.pool1(enc1))
        enc3 = self.encoder3(self.pool2(enc2))
        enc4 = self.encoder4(self.pool3(enc3))

        bottleneck = self.bottleneck(self.pool4(enc4))

        dec4 = self.upconv4(bottleneck)
        dec4 = torch.cat((enc4, dec4), dim=1)
        dec4 = self.decoder4(dec4)
        dec3 = self.upconv3(dec4)
        dec3 = torch.cat((enc3, dec3), dim=1)
        dec3 = self.decoder3(dec3)
        dec2 = self.upconv2(dec3)
        dec2 = torch.cat((enc2, dec2), dim=1)
        dec2 = self.decoder2(dec2)
        dec1 = self.upconv1(dec2)
        dec1 = torch.cat((enc1, dec1), dim=1)
        dec1 = self.decoder1(dec1)

        return self.conv(dec1)

transform中的残差连接

在深度学习中，残差连接（Residual Connection）是一种常用的技巧，被广泛应用在卷积神经网络（CNN）和残差网络（ResNet）中。在Transformer模型中，也使用了残差连接。

残差连接的作用是解决深度神经网络中的梯度消失和梯度爆炸问题。在残差连接中，将输入x与输出F(x)相加，形成残差块，即：

y = x + F(x)

其中，F(x)表示通过神经网络学习得到的函数，x表示输入，y表示输出。

在Transformer模型中，每个编码器和解码器层都包含了两个残差块。其中，第一个残差块连接了多头自注意力层（Multi-Head Attention）和前馈全连接层（Feed-Forward Network），第二个残差块连接了上一个残差块和层归一化层（Layer Normalization）。这种设计可以使得模型更加容易训练和优化，并且有效提高了模型的性能。