Squeeze-and-Excitation Networks 挤压和激励网络

ABSTRACT 摘要

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, much existing work has shown the benefits of enhancing spatial encoding. In this work, we focus on channels and propose a novel architectural unit, which we term the “Squeeze-and-Excitation”(SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at slight computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 eRor to 2.251%, achieving a ∼25% relative improvement over the winning entry of 2016.

卷积神经网络建立在卷积运算的基础上，通过融合局部感受野内的空间信息和通道信息来提取信息特征。为了提高网络的表示能力，许多现有的工作已经显示出增强空间编码的好处。在这项工作中，我们专注于通道，并提出了一种新颖的结构单元，我们称之为“Squeeze-and-Excitation”（SE）块，通过显式地建模通道之间的相互依赖关系，自适应地重新校准通道式的特征响应。通过将这些块堆叠在一起，我们证明了我们可以构建SENet架构，在具有挑战性的数据集中可以进行泛化地非常好。关键的是，我们发现SE块以微小的计算成本为现有的最先进的深层架构产生了显著的性能改进。SENets是我们ILSVRC 2017分类提交的基础，它在该分类赢得​​了第一名，并将top-5错误率显著减少到2.251 %，相对于2016年的获胜成绩取得了25%的相对改进。

Introduction 综述

Convolutional neural networks (CNNs) have proven to be useful models for tackling a wide range of visual tasks . At each convolutional layer in the network, a collection of filters expresses neighbourhood spatial connectivity patterns along input channels---fusing spatial and channel-wise information together within local receptive fields. By interleaving a series of convolutional layers with non-linear activation functions and downsampling operators, CNNs are able to produce image representations that capture hierarchical patterns and attain global theoretical receptive fields. A central theme of computer vision research is the search for more powerful representations that capture only those properties of an image that are most salient for a given task, enabling improved performance. As a widely-used family of models for vision tasks, the development of new neural network architecture designs now represents a key frontier in this search. Recent research has shown that the representations produced by CNNs can be strengthened by integrating learning mechanisms into the network that help capture spatial coRelations between features. One such approach, popularised by the Inception family of architectures , incorporates multi-scale processes into network modules to achieve improved performance. Further work has sought to better model spatial dependencies and incorporate spatial attention into the structure of the network .

卷积神经网络（CNNs）已被证明是解决各种视觉任务的有效模型。对于每个卷积层，沿着输入通道学习一组滤波器来表达局部空间连接模式。换句话说，期望卷积滤波器通过融合空间信息和通道信息进行信息组合，而受限于局部感受野。通过叠加一系列非线性和下采样交织的卷积层，CNN能够捕获具有全局感受野的分层模式作为强大的图像描述。最近的工作已经证明，网络的性能可以通过显式地嵌入学习机制来改善，这种学习机制有助于捕捉空间相关性而不需要额外的监督。Inception架构推广了一种这样的方法，这表明网络可以通过在其模块中嵌入多尺度处理来取得有竞争力的准确度。最近的工作从空间相关性，结合空间注意力等方向进行模型结构探索。

In contrast to these methods, we investigate a diFerent aspect of architectural design —— the channel relationship, by introducing a new architectural unit, which we term the “Squeeze-and-Excitation” (SE) block. Our goal is to improve the representational power of a network by explicitly modelling the interdependencies between the channels of its convolutional features. To achieve this, we propose a mechanism that allows the network to perform feature recalibration, through which it can learn to use global information to selectively emphasise informative features and suppress less useful ones.

与这些方法相反，我们引入新的架构单元，“Squeeze-and-Excitation” (SE)块，我们研究了架构设计的一个不同方向——通道关系。我们的目标是通过对网络的卷积特征通道之间的相互依赖性进行建模，从而提高网络的表示能力。为了达到这个目的，我们提出了一种机制，使网络能够执行特征重新校准，通过该机制，网络可以学习使用全局信息来选择性地强调信息性特征，并抑制不太有用的特征。

The basic structure of the SE building block is illustrated in Fig.1. For any given transformation $ F_{tr} $ mapping the input $ X $ to the feature maps $ U $ where $ U\in\mathbb{R}^{H \times W \times C} $ , (e.g. a convolution or a set of convolutions), we can construct a coResponding SE block to perform feature recalibration as follows. The features \mathbf{U} are first passed through a *squeeze* operation, which aggregates the feature maps across spatial dimensions W \times H to produce a channel descriptor. This descriptor embeds the global distribution of channel-wise feature responses, enabling information from the global receptive field of the network to be leveraged by its lower layers. This is followed by an *excitation* operation, in which sample-specific activations, learned for each channel by a self-gating mechanism based on channel dependence, govern the excitation of each channel. The feature maps \mathbf{U}$ are then reweighted to generate the output of the SE block which can then be fed directly into subsequent layers.

SE构建块的基本结构如图1所示。对于任何给定的变换$ F_{tr} $ : $X → U $, $X \in \mathbb{R}^{W’ \times H’ \times C’}$,$ U\in\mathbb{R}^{H \times W \times C} $ ，(例如卷积或一组卷积)，我们可以构造一个相应的SE块来执行特征重新校准，如下所示。特征U首先通过squeeze操作，该操作跨越空间维度W $\times $ H聚合特征映射来产生通道描述符。这个描述符嵌入了通道特征响应的全局分布，使来自网络全局感受野的信息能够被其较低层利用。这之后是一个excitation操作，其中通过基于通道依赖性的自门控机制（self-gating）为每个通道学习特定采样的激活，控制每个通道的激励。然后特征映射U被重新加权以生成SE块的输出，然后可以将其直接输入到后续层中。

图1. Squeeze-and-Excitation块 A Squeeze-and-Excitation block.

An SE network can be generated by simply stacking a collection of SE building blocks. SE blocks can also be used as a drop-in replacement for the original block at any depth in the architecture. However, while the template for the building block is generic, as we show in Sec. 6.3, the role it performs at diFerent depths adapts to the needs of the network. In the early layers, it learns to excite informative features in a class agnostic manner, bolstering the quality of the shared lower level representations. In later layers, the SE block becomes increasingly specialised, and responds to diFerent inputs in a highly class-specific manner. Consequently, the benefits of feature recalibration conducted by SE blocks can be accumulated through the entire network.

SE网络可以通过简单地堆叠SE模块的集合来生成。SE块也可以直接替换架构中任意深度的原始块。然而，虽然构建块的模板是通用的，正如我们6.3节中展示的那样，但适应于网络的需求，它在不同深度的作用不一样。在前面的层中，它学习以类不可知的方式激发信息特征，增强共享的较低层表示的信息质量。在后面的层中，SE块越来越专业化，并以高度特定类的方式响应不同的输入。因此，整个网络都可以通过SE模块进行特征重新校准进行优化。

The development of new CNN architectures is a challenging engineering task, typically involving the selection of many new hyperparameters and layer configurations. By contrast, the design of the SE block outlined above is simple, and can be used directly with existing state-of-the-art architectures whose convolutional layers can be strengthened by direct replacement with their SE counterparts. Moreover, as shown in Sec. 4, SE blocks are computationally lightweight and impose only a slight increase in model complexity and computational burden. To support these claims, we develop several SENets, namely SE-ResNet, SE-Inception, SE-ResNeXt and SE-Inception-ResNet and provide an extensive evaluation of SENets on the ImageNet 2012 dataset [30]. Further, to demonstrate the general applicability of SE blocks, we also present results beyond ImageNet, indicating that the proposed approach is not restricted to a specific dataset or a task.

新CNN架构的开发是一项具有挑战性的工程任务，通常涉及许多新的超参数和层配置的选择。相比之下，上面概述的SE块的设计是简单的，并且可以直接与现有的最新架构一起使用，其卷积层可以通过直接用对应的SE层来替换从而进行加强。另外，如第四节所示，SE块在计算上是轻量级的，并且在模型复杂性和计算负担方面仅稍微增加。为了支持这些声明，我们开发了一些SENets，即SE-ResNet，SE-Inception，SE-ResNeXt和SE-Inception-ResNet，并在ImageNet 2012数据集上对SENets进行了广泛的评估。此外，为了证明SE块的一般适用性，我们还呈现了ImageNet之外的结果，表明所提出的方法不受限于特定的数据集或任务。

Using SENets, we won the first place in the ILSVRC 2017 classification competition. Our top performing model ensemble achieves a $2.251% top-5 eRor on the test set. This represents a \sim 25% relative improvement in comparison to the winner entry of the previous year (with a top-$5 eRor of $2.991%$). Our models and related materials have been made available to the research community.

使用SENets，我们赢得了ILSVRC 2017分类竞赛的第一名。我们的表现最好的模型集合在测试集上达到了2.251%的top-5错误率。与前一年的获奖者（2.991%的top-5错误率）相比，这表示25%的相对改进。我们的模型和相关材料已经提供给研究界。

Related Work 相关工作

Deep architectures. A wide range of work has shown that restructuring the architecture of a convolutional neural network in a manner that eases the learning of deep features can yield substantial improvements in performance. VGGNets [35] and Inception models [39] demonstrated the benefits that could be attained with an increased depth, significantly outperforming previous approaches on ILSVRC 2014. Batch normalization (BN) [14] improved gradient propagation through deep networks by inserting units to regulate layer inputs stabilising the learning process, which enables further experimentation with a greater depth. He et al. [9, 10] showed that it was effective to train deeper networks by restructuring the architecture to learn residual functions through the use of identity-based skip connections which ease the flow of information across units. More recently, reformulations of the connections between network layers [5, 12] have been shown to further improve the learning and representational properties of deep networks.

深层架构 大量的工作已经表明，以易于学习深度特征的方式重构卷积神经网络的架构可以大大提高性能。VGGNets[35]和Inception模型[39]证明了深度增加可以获得的好处，明显超过了ILSVRC 2014之前的方法。批标准化（BN）[14]通过插入单元来调节层输入稳定学习过程，改善了通过深度网络的梯度传播，这使得可以用更深的深度进行进一步的实验。He等人[9,10]表明，通过重构架构来训练更深层次的网络是有效的，通过使用基于恒等映射的跳跃连接来学习残差函数，从而减少跨单元的信息流动。最近，网络层间连接的重新表示[5,12]已被证明可以进一步改善深度网络的学习和表征属性。

An alternative line of research has explored ways to tune the functional form of the modular components of a network. Grouped convolutions can be used to increase cardinality (the size of the set of transformations) [13, 43] to learn richer representations. Multi-branch convolutions can be interpreted as a generalisation of this concept, enabling more flexible compositions of convolutional operators [14, 38, 39, 40]. Cross-channel correlations are typically mapped as new combinations of features, either independently of spatial structure [6, 18] or jointly by using standard convolutional filters [22] with 1×11×1 convolutions, while much of this work has concentrated on the objective of reducing model and computational complexity. This approach reflects an assumption that channel relationships can be formulated as a composition of instance-agnostic functions with local receptive fields. In contrast, we claim that providing the network with a mechanism to explicitly model dynamic, non-linear dependencies between channels using global information can ease the learning process, and significantly enhance the representational power of the network.

另一种研究方法探索了调整网络模块化组件功能形式的方法。可以用分组卷积来增加基数（一组变换的大小）[13,43]以学习更丰富的表示。多分支卷积可以解释为这个概念的概括，使得卷积算子可以更灵活的组合[14,38,39,40]。跨通道相关性通常被映射为新的特征组合，或者独立的空间结构[6,18]，或者联合使用标准卷积滤波器[22]和1×11×1卷积，然而大部分工作的目标是集中在减少模型和计算复杂度上面。这种方法反映了一个假设，即通道关系可以被表述为具有局部感受野的实例不可知的函数的组合。相比之下，我们声称为网络提供一种机制来显式建模通道之间的动态、非线性依赖关系，使用全局信息可以减轻学习过程，并且显著增强网络的表示能力。

Attention and gating mechanisms. Attention can be viewed, broadly, as a tool to bias the allocation of available processing resources towards the most informative components of an input signal. The development and understanding of such mechanisms has been a longstanding area of research in the neuroscience community [15, 16, 28] and has seen significant interest in recent years as a powerful addition to deep neural networks [20, 25]. Attention has been shown to improve performance across a range of tasks, from localisation and understanding in images [3, 17] to sequence-based models [2, 24]. It is typically implemented in combination with a gating function (e.g. a softmax or sigmoid) and sequential techniques [11, 37]. Recent work has shown its applicability to tasks such as image captioning [4, 44] and lip reading [7], in which it is exploited to efficiently aggregate multi-modal data. In these applications, it is typically used on top of one or more layers representing higher-level abstractions for adaptation between modalities. Highway networks [36] employ a gating mechanism to regulate the shortcut connection, enabling the learning of very deep architectures. Wang et al. [42] introduce a powerful trunk-and-mask attention mechanism using an hourglass module [27], inspired by its success in semantic segmentation. This high capacity unit is inserted into deep residual networks between intermediate stages. In contrast, our proposed SE-block is a lightweight gating mechanism, specialised to model channel-wise relationships in a computationally efficient manner and designed to enhance the representational power of modules throughout the network.

注意力和门机制。从广义上讲，可以将注意力视为一种工具，将可用处理资源的分配偏向于输入信号的信息最丰富的组成部分。这种机制的发展和理解一直是神经科学社区的一个长期研究领域[15,16,28]，并且近年来作为一个强大补充，已经引起了深度神经网络的极大兴趣[20,25]。注意力已经被证明可以改善一系列任务的性能，从图像的定位和理解[3,17]到基于序列的模型[2,24]。它通常结合门功能（例如softmax或sigmoid）和序列技术来实现[11,37]。最近的研究表明，它适用于像图像标题[4,44]和口头阅读[7]等任务，其中利用它来有效地汇集多模态数据。在这些应用中，它通常用在表示较高级别抽象的一个或多个层的顶部，以用于模态之间的适应。高速网络[36]采用门机制来调节快捷连接，使得可以学习非常深的架构。王等人[42]受到语义分割成功的启发，引入了一个使用沙漏模块[27]的强大的trunk-and-mask注意力机制。这个高容量的单元被插入到中间阶段之间的深度残差网络中。相比之下，我们提出的SE块是一个轻量级的门机制，专门用于以计算有效的方式对通道关系进行建模，并设计用于增强整个网络中模块的表示能力。

Squeeze-and-Excitation Blocks SE模块

A Squeeze-and-Excitation block is a computational unit which can be built upon a transformation $ F_{tr} $ mapping an input $ x\in R^{H' \times W' \times C'} $ to feature maps $ U\in R^{H \times W \times C} $ . In the notation that follows we take $ F_{tr} $ to be a convolutional operator and use $V= [v_1, v_2, \dots, v_c] $to denote the learned set of filter kernels, where $ v_c $ refers to the parameters of the filter. We can then write the outputs as $ U=[u_1,u_2, \dots, U_c] $ , where

Squeeze-and-Excitation块是一种计算单元，可以为任何给定的变换构建：$F_{tr}:X→U $，$x\in R^{H' \times W' \times C'} $，$U\in R^{H \times W \times C} $。为了简化说明，在接下来的表示中，我们将$F_{tr} $作为卷积运算符使用$V= [v_1, v_2, \dots, v_c] $表示学习到的一组滤波器核，其中$v_c $ 是指的是第c个滤波器的参数。然后，我们可以将输出写成 $ U=[u_1,u_2, \dots, U_c] $，其中

$$ U_c = v_c ∗ X = \sum_{s=1}^{C'}v^s_c∗ x^s。 $$

Here $ \ast $ denotes convolution, $ v_c = [v^1_c, v^2_c, \dots, v_c = 0_c = 1_c = 2_c = 3 $ , $ x = [x^1, x^2, \dots, x = 0 = 1 = 2 $ and . $ v^s_c $ is a $ 2 $ D spatial kernel representing a single channel of $ v_c $ that acts on the coResponding channel of $ x $ . To simplify the notation, bias terms are omitted. Since the output is produced by a summation through all channels, channel dependencies are implicitly embedded in $ v_c $ , but are entangled with the local spatial coRelation captured by the filters. The channel relationships modelled by convolution are inherently implicit and local (except the ones at top-most layers). We expect the learning of convolutional features to be enhanced by explicitly modelling channel interdependencies, so that the network is able to increase its sensitivity to informative features which can be exploited by subsequent transformations. Consequently, we would like to provide it with access to global information and recalibrate filter responses in two steps, and , before they are fed into the next transformation. A diagram illustrating the structure of an SE block is shown in Fig.splash.

这里$\ast $表示卷积，$v_c = [v^1_c, v^2_c, \dots, v^C'_c] $，$X = [x^1, x^2, \dots, x^C'_c $和。 $v^s_c $是2d空间核，表示单个通道的$v_c $，作用于对应的通道X。由于输出由通过所有通道的求和产生，因此通道依赖性被隐式地嵌入到$v_c $中，但是这些依赖性与滤波器捕获的空间相关性纠缠在一起。我们的目标是确保能够提高网络对信息特征的敏感度，以便后续转换可以利用这些功能，并抑制不太有用的功能。我们建议通过显式建模通道依赖性来实现这一点，以便在进入下一个转换之前通过两步重新校准滤波器响应，两步为：squeeze和excitation。SE构建块的图如图所示。

Squeeze: Global Information Embedding Squeeze：全局信息嵌入

In order to tackle the issue of exploiting channel dependencies, we first consider the signal to each channel in the output features. Each of the learned filters operate with a local receptive field and consequently each unit of the transformation output U is unable to exploit contextual information outside of this region. This is an issue that becomes more severe in the lower layers of the network whose receptive field sizes are small.

为了解决利用通道依赖性的问题，我们首先考虑输出特征中每个通道的信号。每个学习到的滤波器都对局部感受野进行操作，因此变换输出U的每个单元都无法利用该区域之外的上下文信息。在网络较低的层次上其感受野尺寸很小，这个问题变得更严重。

To mitigate this problem, we propose to global spatial information into a channel descriptor. This is achieved by using global average pooling to generate channel-wise statistics. Formally, a statistic $ z\in R^{C} $ is generated by shrinking $ U $ through its spatial dimensions $ H \times W $ , such that the $ c $ -th element of $ \z $ is calculated by:

为了缓解此问题，我们提出将全局空间信息压缩成一个通道描述符。这是通过使用全局平均池化生成通道统计实现的。形式上，在空间维度$ H \times W $ 收缩U统计生成$ z\in R^{C} $ ，使得$z$的第$ c$ 个元素通过如下公式计算：
$$ z_c = F_{sq}(U_c) = \frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W} u_c(i,j). $$

Discussion.The output of the transformation $ U $ can be interpreted as a collection of the local descriptors whose statistics are expressive for the whole image. Exploiting such information is prevalent in prior feature engineering work . We opt for the simplest aggregation technique, global average pooling, noting that more sophisticated strategies could be employed here as well.

讨论:转换$U $的输出可以被解释为局部描述子的集合，其统计信息对于整个图像呈现。利用此类信息在先前的特征工程工作中是普遍使用的。我们选择最简单的全局平均池化，注意到这里可以使用更复杂的策略。

Excitation: Adaptive Recalibration Excitation：自适应重新校准

To make use of the information aggregated in the operation, we follow it with a second operation which aims to fully capture channel-wise dependencies. To fulfil this objective, the function must meet two criteria: first, it must be flexible (in particular, it must be capable of learning a nonlinear interaction between channels) and second, it must learn a non-mutually-exclusive relationship since we would like to ensure that multiple channels are allowed to be emphasised (rather than enforcing a one-hot activation). To meet these criteria, we opt to employ a simple gating mechanism with a sigmoid activation:

为了利用压缩操作中汇聚的信息，我们接下来通过第二个操作来全面捕获通道依赖性。为了实现这个目标，这个功能必须符合两个标准：第一，它必须是灵活的（特别是它必须能够学习通道之间的非线性交互）；第二，它必须学习一个非互斥的关系，因为独热激活相反，这里允许强调多个通道。为了满足这些标准，我们选择采用一个简单的门机制，并使用sigmoid激活：

$$ s = F_{ex}(z, W) = \sigma(g(z, W)) = \sigma(W_2\delta(W_1z)), $$

where $ \delta $ refers to the ReLU function, $ W_1 \in R^{\frac{C}{r} \times C} $ and $ W_2 \in R^{C \times \frac{C}{r}} $ . To limit model complexity and aid generalisation, we parameterise the gating mechanism by forming a bottleneck with two fully-connected (FC) layers around the non-linearity, i.e. a dimensionality-reduction layer with reduction ratio $ r $ (this parameter choice is discussed in Sectionreduction), a ReLU and then a dimensionality-increasing layer returning to the channel dimension of the transformation output $ U $ . The final output of the block is obtained by rescaling $ U $ with the activations $ s $ :

其中$\delta $指的是Relu功能，$W_1 \in R^{\frac{C}{r} \times C} $和$\in R^{C \times \frac{C}{r}} $。为了限制模型复杂性和辅助泛化，我们通过形成围绕非线性的两个全连接层（Fc）的瓶颈来参数化门机制，即降维层参数为W1，降维比例为r（我们把它设置为16，这个参数选择在6.3节中讨论），一个ReLU，然后是一个参数为W2的升维层。块的最终输出通过重新调节带有激活的变换输出U得到：

$$ \widetilde{x}c = F{scale}(u_c, s_c) = s_c,u_c, $$

where X and $ F_{scale}(U_c, s_c) $ refers to channel-wise multiplication between the scalar $ s_c $ and the feature map $ U_c \in R^{H \times W} $ .

其中X 和 $F_{scale}(U_c, s_c) $指的是 标量$ s_c $和特征映射$U_c \in R^{H \times W} $之间的对应通道乘积。

The excitation operator maps the input-specific descriptor $ z $ to a set of channel weights. In this regard, SE blocks intrinsically introduce dynamics conditioned on the input, which can be regarded as a self-attention function on channels whose relationships are not confined to the local receptive field the convolutional filters are responsive to.

激励运算符将输入特定的描述符$ z $映射到一组频道权重。在这方面，SE块本质上引入了以输入为条件的动态特性，有助于提高特征辨别力。

3.3. Exemplars: SE-Inception and SE-ResNet 示例：SE-Inception和SE-ResNet

The flexibility of the SE block means that it can be directly applied to transformations beyond standard convolutions. To illustrate this point, we develop SENets by integrating SE blocks into two popular network families of architectures, Inception and ResNet. SE blocks are constructed for the Inception network by taking the transformation $ F_{tr} $to be an entire Inception module (see Fig.2). By making this change for each such module in the architecture, we construct an SE-Inception network.

SE块的灵活性意味着它可以直接应用于标准卷积之外的变换。为了说明这一点，我们通过将SE块集成到两个流行的网络架构系列Inception和ResNet中来开发SENets。通过将变换$ F_{tr} $看作一个整体的Inception模块（参见图2），为Inception网络构建SE块。通过对架构中的每个模块进行更改，我们构建了一个SE-Inception网络。

Figure 2. The schema of the original Inception module (left) and the SE-Inception module (right).
图2。最初的Inception模块架构(左)和SE-Inception模块架构(右)。

Residual networks and their variants have shown to be highly effective at learning deep representations. We develop a series of SE blocks that integrate with ResNet [9], ResNeXt [43] and Inception-ResNet [38] respectively. Fig.3 depicts the schema of an SE-ResNet module. Here, the SE block transformation $ F_{tr} $ is taken to be the non-identity branch of a residual module. Squeeze and excitation both act before summation with the identity branch.

Figure 3. The schema of the original Residual module (left) and the SE-ResNet module (right).
图3。 最初的Residual模块架构(左)和SE-ResNet模块架构(右)。

残留网络及其变种已经证明在学习深度表示方面非常有效。我们开发了一系列的SE块，分别与ResNet[9]，ResNeXt[43]和Inception-ResNet[38]集成。图3描述了SE-ResNet模块的架构。在这里，SE块变换FtrFtr被认为是残差模块的非恒等分支。压缩和激励都在恒等分支相加之前起作用。

Model and Computational Complexity 模型和计算复杂度

An SENet is constructed by stacking a set of SE blocks. In practice, it is generated by replacing each original block (i.e. residual block) with its corresponding SE counterpart (i.e. SE-residual block). We describe the architecture of SE-ResNet-50 and SE-ResNeXt-50 in Table 1.

SENet通过堆叠一组SE块来构建。实际上，它是通过用原始块的SE对应部分（即SE残差块）替换每个原始块（即残差块）而产生的。我们在表1中描述了SE-ResNet-50和SE-ResNeXt-50的架构。

Table 1. (Left) ResNet-50. (Middle) SE-ResNet-50. (Right) SE-ResNeXt-50 with a 32×4d32×4d template. The shapes and operations with specific parameter settings of a residual building block are listed inside the brackets and the number of stacked blocks in a stage is presented outside. The inner brackets following by fc indicates the output dimension of the two fully connected layers in a SE-module.

表1。(左)ResNet-50，(中)SE-ResNet-50，(右)具有32×4d32×4d模板的SE-ResNeXt-50。在括号内列出了残差构建块特定参数设置的形状和操作，并且在外部呈现了一个阶段中堆叠块的数量。fc后面的内括号表示SE模块中两个全连接层的输出维度。

For the proposed SE block design to be of practical use, it must oFer a good trade-oF between improved performance and increased model complexity. To illustrate the computational burden associated with the module, we consider a comparison between ResNet-50 and SE-ResNet-50 as an example. ResNet-50 requires $ {\sim}3.86 $ GFLOPs in a single forward pass for a $ 224\times224 $ pixel input image. Each SE block makes use of a global average pooling operation in the phase and two small FC layers in the phase, followed by an inexpensive channel-wise scaling operation. In the aggregate, when setting the reduction ratio $ r $ (introduced in Sectionadaptive-recal) to $ 16 $ , SE-ResNet-50 requires $ {\sim}3.87 $ GFLOPs, coResponding to a $ 0.26% $ relative increase over the original ResNet-50. In exchange for this slight additional computational burden, the accuracy of surpasses that of ResNet-50 and indeed, approaches that of a deeper ResNet-101 network requiring $ {\sim}7.58 $ GFLOPs (Tableimagenet-results). In practical terms, a single pass forwards and backwards through ResNet-50 takes $ 190 $ ms, compared to $ 209 $ ms for SE-ResNet-50 with a training minibatch of $ 256 $ images (both timings are performed on a server with $ 8 $ NVIDIA Titan X GPUs). We suggest that this represents a reasonable runtime overhead, which may be further reduced as global pooling and small inner-product operations receive further optimisation in popular GPU libraries. Due to its importance for embedded device applications, we further benchmark CPU inference time for each model: for a $ 224\times 224 $ pixel input image, ResNet-50 takes $ 164 $ ms in comparison to $ 167 $ ms for SE-ResNet-50.

为提出的SE块设计具有实际使用，它必须在提高性能和提高模型复杂性之间提供良好的权衡。为了说明与模块相关的计算负担，我们考虑作为示例的Reset-50和SE-Resnet-50之间的比较。 RESET-50需要${\sim}3.86 $ GFLOP在单个向前通行证中进行$ 224\times224 $像素输入图像。每个SE块在阶段中使用相位和两个小FC层的全局平均池操作，然后是廉价的通道 - 方向缩放操作。在聚合时，在将$ r $（在SEENADAPTIVE-RECAL中引入）到$ 16 $时，SE-RESNET-50需要${\sim}3.87 $ GFLOP，对应于$ 0.26% $相对增加原始RESNET - 50。为了换取这种轻微的额外计算负担，超越Reset-50且实际上的准确性接近了更深的Reset-101网络，需要${\sim}7.58 $ GFLOPS（TableImageNet-excupt）。实际上，通过RESET-50通过RESET-50向后和向后向后，与$ 209 $ MS相比，对于SE-RESNET-50，具有$ 256 $图像的训练传单（两个定时在服务器上执行两个定时）相比$ 8 $ nvidia titan x gpus）。我们建议这代表了合理的运行时间开销，这可能进一步减少，因为全球池和小型内部产品运营接收到流行的GPU库中的进一步优化。由于其对嵌入式设备应用的重要性，我们进一步的每个型号的基准CPU推理时间：对于$ 224\times 224 $像素输入图像，RESET-50与$ 167 $ MS进行$ 164 $ MS，用于SE-RESET- 50。

Table 2. Single-crop error rates (%) on the ImageNet validation set and complexity comparisons. The original column refers to the results reported in the original papers. To enable a fair comparison, we re-train the baseline models and report the scores in the re-implementation column. The SENet column refers the corresponding architectures in which SE blocks have been added. The numbers in brackets denote the performance improvement over the re-implemented baselines. † indicates that the model has been evaluated on the non-blacklisted subset of the validation set (this is discussed in more detail in [38]), which may slightly improve results.
表2。ImageNet验证集上的单裁剪图像错误率（％）和复杂度比较。original列是指原始论文中报告的结果。为了进行公平比较，我们重新训练了基准模型，并在re-implementation列中报告分数。SENet列是指已添加SE块后对应的架构。括号内的数字表示与重新实现的基准数据相比的性能改善。†表示该模型已经在验证集的非黑名单子集上进行了评估（在[38]中有更详细的讨论），这可能稍微改善结果。

In practice, with a training mini-batch of 256 images, a single pass forwards and backwards through ResNet-50 takes 190190ms, compared to 209ms for SE-ResNet-50 (both timings are performed on a server with 88 NVIDIA Titan X GPUs). We argue that it is a reasonable overhead as global pooling and small inner-product operations are less optimised in existing GPU libraries. Moreover, due to its importance for embedded device applications, we also benchmark CPU inference time for each model: for a 224×224pixel input image, ResNet-50 takes 164ms, compared to for SE-ResNet-50. The small additional computational overhead required by the SE block is justified by its contribution to model performance (discussed in detail in Sec. 6).

在实践中，训练的批数据大小为256张图像，ResNet-50的一次前向传播和反向传播花费190 ms，而SE-ResNet-50则花费209 ms（两个时间都在具有88个NVIDIA Titan X GPU的服务器上执行）。我们认为这是一个合理的开销，因为在现有的GPU库中，全局池化和小型内积操作的优化程度较低。此外，由于其对嵌入式设备应用的重要性，我们还对每个模型的CPU推断时间进行了基准测试：对于224×224像素的输入图像，ResNet-50花费了164ms，相比之下，SE-ResNet-50花费了167ms。SE块所需的小的额外计算开销对于其对模型性能的贡献来说是合理的（在第6节中详细讨论）。

Next, we consider the additional parameters introduced by the proposed block. All additional parameters are contained in the two fully connected layers of the gating mechanism, which constitute a small fraction of the total network capacity. More precisely, the number of additional parameters introduced is given by:

where rdenotes the reduction ratio (we set r to 16 in all our experiments), S refers to the number of stages (where each stage refers to the collection of blocks operating on feature maps of a common spatial dimension), Cs denotes the dimension of the output channels for stage s and Nsrefers to the repeated block number. In total, SE-ResNet-50 introduces ∼∼2.5 million additional parameters beyond the ∼∼25 million parameters required by ResNet-50, corresponding to a ∼10% increase in the total number of parameters. The majority of these additional parameters come from the last stage of the network, where excitation is performed across the greatest channel dimensions. However, we found that the comparatively expensive final stage of SE blocks could be removed at a marginal cost in performance (<0.1% top-1 error on ImageNet dataset) to reduce the relative parameter increase to ∼4%, which may prove useful in cases where parameter usage is a key consideration.
其中r表示减少比率（我们在所有的实验中将r设置为16），S指的是阶段数量（每个阶段是指在共同的空间维度的特征映射上运行的块的集合），Cs表示阶段s的输出通道的维度，Ns表示重复的块编号。总的来说，SE-ResNet-50在ResNet-50所要求的2500万参数之外引入了250万附加参数，相对增加了∼10%的参数总数量。这些附加参数中的大部分来自于网络的最后阶段，其中激励在最大的通道维度上执行。然而，我们发现SE块相对昂贵的最终阶段可以在性能的边际成本（ImageNet数据集上<0.1%的top-1错误率）上被移除，将相对参数增加减少到∼4%，这在参数使用是关键考虑的情况下可能证明是有用的。

IMPLEMENTATION

During training, we follow standard practice and perform data augmentation with random-size cropping [39] to 224×224 pixels (299×299 for Inception-ResNet-v2 [38] and SE-Inception-ResNet-v2) and random horizontal flipping. Input images are normalised through mean channel subtraction. In addition, we adopt the data balancing strategy described in [32] for mini-batch sampling to compensate for the uneven distribution of classes. The networks are trained on our distributed learning system “ROCS” which is capable of handing efficient parallel training of large networks. Optimisation is performed using synchronous SGD with momentum 0.9 and a mini-batch size of 1024 (split into sub-batches of 32 images per GPU across 4 servers, each containing 8 GPUs). The initial learning rate is set to 0.6 and decreased by a factor of 10 every 30 epochs. All models are trained for 100 epochs from scratch, using the weight initialisation strategy described in [8].

在训练过程中，我们遵循标准的做法，使用随机大小裁剪[39]到224×224像素（299×299用于Inception-ResNet-v2[38]和SE-Inception-ResNet-v2）和随机的水平翻转进行数据增强。输入图像通过通道减去均值进行归一化。另外，我们采用[32]中描述的数据均衡策略进行小批量采样，以补偿类别的不均匀分布。网络在我们的分布式学习系统“ROCS”上进行训练，能够处理大型网络的高效并行训练。使用同步SGD进行优化，动量为0.9，小批量数据的大小为1024（在4个服务器的每个GPU上分成32张图像的子批次，每个服务器包含8个GPU）。初始学习率设为0.6，每30个迭代周期减少10倍。使用[8]中描述的权重初始化策略，所有模型都从零开始训练100个迭代周期。

Experiments 实验

In this section we conduct extensive experiments on the ImageNet 2012 dataset [30] for the purposes: first, to explore the impact of the proposed SE block for the basic networks with different depths and second, to investigate its capacity of integrating with current state-of-the-art network architectures, which aim to a fair comparison between SENets and non-SENets rather than pushing the performance. Next, we present the results and details of the models for ILSVRC 2017 classification task. Furthermore, we perform experiments on the Places365-Challenge scene classification dataset [48] to investigate how well SENets are able to generalise to other datasets. Finally, we investigate the role of excitation and give some analysis based on experimental phenomena.

在这一部分，我们在ImageNet 2012数据集上进行了大量的实验[30]，其目的是：首先探索提出的SE块对不同深度基础网络的影响；其次，调查它与最先进的网络架构集成后的能力，旨在公平比较SENets和非SENets，而不是推动性能。接下来，我们将介绍ILSVRC 2017分类任务模型的结果和详细信息。此外，我们在Places365-Challenge场景分类数据集[48]上进行了实验，以研究SENets是否能够很好地泛化到其它数据集。最后，我们研究激励的作用，并根据实验现象给出了一些分析。

Image Classification 图像分类

The ImageNet 2012 dataset is comprised of 1.28 million training images and 50K validation images from 1000 classes. We train networks on the training set and report the top-1 and the top-5 errors using centre crop evaluations on the validation set, where 224×224 pixels are cropped from each image whose shorter edge is first resized to 256 (299×299 from each image whose shorter edge is first resized to 352 for Inception-ResNet-v2 and SE-Inception-ResNet-v2).

ImageNet 2012数据集包含来自1000个类别的128万张训练图像和5万张验证图像。我们在训练集上训练网络，并在验证集上使用中心裁剪图像评估来报告top-1和top-5错误率，其中每张图像短边首先归一化为256，然后从每张图像中裁剪出224×224个像素，（对于Inception-ResNet-v2和SE-Inception-ResNet-v2，每幅图像的短边首先归一化到352，然后裁剪出299×299个像素）。

Network depth. We first compare the SE-ResNet against a collection of standard ResNet architectures. Each ResNet and its corresponding SE-ResNet are trained with identical optimisation schemes. The performance of the different networks on the validation set is shown in Table 2, which shows that SE blocks consistently improve performance across different depths with an extremely small increase in computational complexity.

**网络深度。**我们首先将SE-ResNet与一系列标准ResNet架构进行比较。每个ResNet及其相应的SE-ResNet都使用相同的优化方案进行训练。验证集上不同网络的性能如表2所示，表明SE块在不同深度上的网络上计算复杂度极小增加，始终提高性能。

Remarkably, SE-ResNet-50 achieves a single-crop top-5 validation error of 6.62%, exceeding ResNet-50 (7.48%) by 0.86% and approaching the performance achieved by the much deeper ResNet-101 network (6.52% top-5 error) with only half of the computational overhead (3.87 GFLOPs vs. 7.58 GFLOPs). This pattern is repeated at greater depth, where SE-ResNet-101 (6.07% top-5 error) not only matches, but outperforms the deeper ResNet-152 network (6.34% top-5 error) by 0.27%. Fig. 4 depicts the training and validation curves of SE-ResNets and ResNets, respectively. While it should be noted that the SE blocks themselves add depth, they do so in an extremely computationally efficient manner and yield good returns even at the point at which extending the depth of the base architecture achieves diminishing returns. Moreover, we see that the performance improvements are consistent through training across a range of different depths, suggesting that the improvements induced by SE blocks can be used in combination with adding more depth to the base architecture.
值得注意的是，SE-ResNet-50实现了单裁剪图像6.62%的top-5验证错误率，超过了ResNet-50（7.48%）0.86%，接近更深的ResNet-101网络（6.52%的top-5错误率），且只有ResNet-101一半的计算开销（3.87 GFLOPs vs. 7.58 GFLOPs）。这种模式在更大的深度上重复，SE-ResNet-101（6.07%的top-5错误率）不仅可以匹配，而且超过了更深的ResNet-152网络（6.34%的top-5错误率）。图4分别描绘了SE-ResNets和ResNets的训练和验证曲线。虽然应该注意SE块本身增加了深度，但是它们的计算效率极高，即使在扩展的基础架构的深度达到收益递减的点上也能产生良好的回报。而且，我们看到通过对各种不同深度的训练，性能改进是一致的，这表明SE块引起的改进可以与增加基础架构更多深度结合使用。

Figure 4. Training curves on ImageNet. (Left): ResNet-50 and SE-ResNet-50; (Right): ResNet-152 and SE-ResNet-152.
图4。ImageNet上的训练曲线。(左)：ResNet-50和SE-ResNet-50；(右)：ResNet-152和SE-ResNet-152。

Integration with modern architectures. We next investigate the effect of combining SE blocks with another two state-of-the-art architectures, Inception-ResNet-v2 [38] and ResNeXt [43]. The Inception architecture constructs modules of convolutions as multibranch combinations of factorised filters, reflecting the Inception hypothesis [6] that spatial correlations and cross-channel correlations can be mapped independently. In contrast, the ResNeXt architecture asserts that richer representations can be obtained by aggregating combinations of sparsely connected (in the channel dimension) convolutional features. Both approaches introduce prior-structured correlations in modules. We construct SENet equivalents of these networks, SE-Inception-ResNet-v2 and SE-ResNeXt (the configuration of SE-ResNeXt-50 (32×4d) is given in Table 1). Like previous experiments, the same optimisation scheme is used for both the original networks and their SENet counterparts.

**与现代架构集成。**接下来我们将研究SE块与另外两种最先进的架构Inception-ResNet-v2[38]和ResNeXt[43]的结合效果。Inception架构将卷积模块构造为分解滤波器的多分支组合，反映了Inception假设[6]，可以独立映射空间相关性和跨通道相关性。相比之下，ResNeXt体架构断言，可以通过聚合稀疏连接（在通道维度中）卷积特征的组合来获得更丰富的表示。两种方法都在模块中引入了先前结构化的相关性。我们构造了这些网络的SENet等价物，SE-Inception-ResNet-v2和SE-ResNeXt（表1给出了SE-ResNeXt-50（32×4d）的配置）。像前面的实验一样，原始网络和它们对应的SENet网络都使用相同的优化方案。

The results given in Table 2 illustrate the significant performance improvement induced by SE blocks when introduced into both architectures. In particular, SE-ResNeXt-50 has