深度学习论文阅读图像分类篇（六）：SENet《Squeeze-and-Excitation Networks》_论文深度学习图

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 channelwise 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 error 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表明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架构单元，我们称之为“Squeezeand-Excitation”（SE）模块，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 模块以微小的计算成本 为现有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 年的获胜团队取得了约 25%的相对 改进。

1. Introduction 引言

  Convolutional neural networks (CNNs) have proven to be effective models for tackling a variety of visual tasks [19, 23, 29, 41]. For each convolutional layer, a set of filters are learned to express local spatial connectivity patterns along input channels. In other words, convolutional filters are expected to be informative combinations by fusing spatial and channel-wise information together, while restricted in local receptive fields. By stacking a series of convolutional layers interleaved with non-linearities and downsampling, CNNs are capable of capturing hierarchical patterns with global receptive fields as powerful image descriptions. Recent work has demonstrated the performance of networks can be improved by explicitly embedding learning mechanisms that help capture spatial correlations without requiring additional supervision. One such approach was popularised by the Inception architectures [14, 39], which showed that the network can achieve competitive accuracy by embedding multi-scale processes in its modules. More recent work has sought to better model spatial dependence [1, 27] and incorporate spatial attention [17].
  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[19,23,29,41]。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 息和信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[14,39]， 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。最近的工作在寻找更好地模型空间依赖[1,27]，结合空间注 意力[17]。
  In contrast to these methods, we investigate a different 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，通过引入新的架构单元，我们称之为“Squeezeand-Excitation”(SE)块，我们研究了架构设计的一个不同方向——通 道关系。我们的目标是通过显式地建模卷积特征通道之间的相互依赖 性来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，通过这种机制可以学习使用全局信息 来选择性地强调信息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 Ftr:
X→UFtr:X → U, X ∈ RW′×H′×C′,U ∈ RW×H×CX∈RW′×H′×C′,U∈RW×H×C, (e.g. a convolution or a set of convolutions), we can construct a corresponding SE block to perform feature recalibration as follows. The features UU are first passed through a squeeze operation, which aggregates the feature maps across spatial dimensions W×HW× 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 selfgating mechanism based on channel dependence, govern the excitation of each channel. The feature maps UU 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 Ftr:X→UFtr:X→U, X∈RW′×H′×C′,U∈RW×H×CX∈RW′×H′× C′,U∈RW×H×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 UU 首先通过 squeeze 操作，该操作跨越空间维度 W×HW×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机制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 UU 被重新加权以生成 SE 块的输出，然后可以将其直接输入到随后的层中。

  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 different 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 different 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 构建块的集合来生成。SE 块也 可以用作架构中任意深度的原始块的直接替换。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，但它在不同深度的作 用适应于网络的需求。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质量。在后面的层中，SE 块越 来越专业化，并以高度类特定的方式响应不同的输入。因此，SE 块 进行特征重新校准的好处可以通过整个网络进行累积。
  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, SEInception, 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[30]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%2.251% top-5 error on the test set. This represents a ∼25%∼25% relative improvement in comparison to the winner entry of the previous year (with a top-55 error of 2.991%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%2.251%的 top-5 错误 率。与前一年的获奖者（2.991%2.991%的 top-5 错误率）相比，这表 示∼25%∼25%的相对改进。我们的模型和相关材料已经提供给研究界。

2. 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 instanceagnostic functions with local receptive fields. In contrast, we claim that providing the network with a mechanism to explicitly model dynamic, nonlinear 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。

3. Squeeze-and-Excitation Blocks

  The Squeeze-and-Excitation block is a computational unit which can be constructed for any given transformation Ftr:
X→U,X∈RW′×H′×C′,U ∈RW×H×CFtr:X→U,X∈RW′×H′×C′,U∈RW×H×C. For simplicity of exposition, in the notation that follows we take FtrFtr to be a standard convolutional operator. Let V=[v1,v2,…,vC]V=[v1,v2, … ,vC] denote the learned set of filter kernels, where vcvc refers to the parameters of the cc-th filter. We can then write the outputs of FtrFtr as U=[u1,u2,…,uC]U=[u1,u2,…,uC] where uc=vc∗X=∑s=1C′vsc∗xs.uc=vc∗X=∑s=1C′vcs∗xs. Here ∗ ∗ denotes convolution, vc=[v1c,v2c,…,vC′c]vc=[vc1,vc2,…,vcC′] and X=[x1,x2,… ,xC′]X=[x1,x2,…,xC′] (to simplify the notation, bias terms are omitted). Here vscvcs is a 22D spatial kernel, and therefore represents a single channel of vcvc which acts on the corresponding channel of XX. Since the output is produced by a summation through all channels, the channel dependencies are implicitly embedded in vcvc, but these dependencies are entangled with the spatial correlation captured by the filters. Our goal is to ensure that the network is able to increase its sensitivity to informative features so that they can be exploited by subsequent transformations, and to suppress less useful ones. We propose to achieve this by explicitly modelling channel interdependencies to recalibrate filter responses in two steps, squeeze and excitation, before they are fed into next transformation. A diagram of an SE building block is shown in Fig.1.
  Squeeze-and-Excitation 块是一个计算单元，可以为任何给定的变 换构建：Ftr:X→U,X∈RW′×H′×C′,U∈RW×H×CFtr:X→U,X∈RW′ ×H′×C′,U∈RW×H×C。为了简化说明，在接下来的表示中，我 们将 FtrFtr 看作一个标准的卷积算子。V=[v1,v2,…,vC]V=[v1,v2,…,vC] 表示学习到的一组滤波器核，vcvc 指的是第 cc 个滤波器的参数。然 后我们可以将 FtrFtr 的输出写作 U=[u1,u2,…,uC]U=[u1,u2,…,uC]，其 中 uc=vc∗X=∑s=1C′vsc∗xs.uc=vc∗X=∑s=1C′vcs∗xs. 这里∗ ∗表示卷积，vc=[v1c,v2c,…,vC′c]vc=[vc1,vc2,…,vcC′]， X=[x1,x2,…,xC′]X=[x1,x2,…,xC′]（为了简洁表示，忽略偏置项）。这 里 vscvcs 是 22D 空间核，因此表示 vcvc 的一个单通道，作用于对应 的通道 XX。由于输出是通过所有通道的和来产生的，所以通道依赖 性被隐式地嵌入到 vcvc 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 1 所示。

3.1. Squeeze: Global Information Embedding 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 UU 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 UU 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 squeeze global spatial information into a channel descriptor. This is achieved by using global average pooling to generate channel-wise statistics. Formally, a statistic z ∈ RCz ∈ RC is generated by shrinking UU through spatial dimensions W×HW×H, where the cc-th element of zz is calculated by: zc=Fsq(uc)=1W×H∑i=1W∑j=1Huc(i,j).zc=Fsq(uc)=1W×H∑i=1W ∑j=1Huc(i,j).
  为了减轻这个问题，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，统 计 z∈RCz∈RC 是通过在空间维度 W×HW×H 上收缩 UU 生成的， 其中 zz 的第 cc 个元素通过下式计算： zc=Fsq(uc)=1W×H∑i=1W∑j=1Huc(i,j).zc=Fsq(uc)=1W×H∑i=1W ∑j=1Huc(i,j).
  Discussion. The transformation output UU can be interpreted as a collection of the local descriptors whose statistics are expressive for the whole image. Exploiting such information is prevalent in feature engineering work [31, 34, 45]. We opt for the simplest, global average pooling, while more sophisticated aggregation strategies could be employed here as well.
  讨论。转换输出 UU 可以被解释为局部描述子的集合，这些描述 子的统计信息对于整个图像来说是有表现力的。特征工程工作中 [31,34,45]普遍使用这些信息。我们选择最简单的全局平均池化，同时 也可以采用更复杂的汇聚策略。

3.2. Excitation: Adaptive Recalibration 自适应重新校正

  To make use of the information aggregated in the squeeze operation, we follow it with a second operation which aims to fully capture channelwise 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 as multiple channels are allowed to be emphasised opposed to one-hot activation. To meet these criteria, we opt to employ a simple gating mechanism with a sigmoid activation: s=Fex(z,W)=σ(g(z,W))=σ(W2δ(W1z))s=Fex(z,W)=σ(g(z,W))=σ(W 2δ(W1z)) where δδ refers to the ReLU[26] function, W1 ∈ RCr×CW1 ∈ RCr×C and W2∈RC×CrW2∈RC×Cr. 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 parameters W1W1 with reduction ratio rr (we set it to be 16, and this parameter choice is discussed in Sec.6.3), a ReLU and then a dimensionality-increasing layer with parameters W2W2. The final output of the block is obtained by rescaling the transformation output UU with the activations: x˜c=Fscale(uc,sc)=sc⋅ucx˜c=Fscale(uc,sc)=sc⋅uc where X˜=[x˜1,x˜2,…,x˜C]X_=[x1,x_2,…,xC] and Fscale(uc,sc) Fscale(uc,sc) refers to channel-wise multiplication between the feature map uc∈RW×Huc∈RW×Hand the scalar scsc.
  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=Fex(z,W)=σ(g(z,W))=σ(W2δ(W1z))s=Fex(z,W)=σ(g(z,W))=σ(W 2δ(W1z)) ，其中 δδ 是指 ReLU[26]函数，W1∈RCr×CW1∈RCr×C 和 W2 ∈RC×CrW2∈RC×Cr。为了限制模型复杂度和辅助泛化，我们通过在 非线性周围形成两个全连接（FC）层的瓶颈来参数化门机制，即降维 层参数为 W1W1，降维比例为 rr（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 W2W2 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 UU 得到： x˜c=Fscale(uc,sc)=sc⋅ucx˜c=Fscale(uc,sc)=sc⋅uc 其 中 X˜=[x˜1,x˜2,…,x˜C]X_=[x1,x_2,…,xC] 和 Fscale(uc,sc)Fscale(uc,sc)指的是特征映射 uc∈RW×Huc∈RW×H 和标 量 scsc 之间的对应通道乘积。
  Discussion. The activations act as channel weights adapted to the input-specific descriptor zz. In this regard, SE blocks intrinsically introduce dynamics conditioned on the input, helping to boost feature discriminability.
  讨论。激活作为适应特定输入描述符 zz 的通道权重。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 模型：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 FtrFtr 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 FtrFtr 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。

  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 FtrFtr is taken to be the non-identity branch of a residual module. Squeeze and excitation both act before summation with the identity branch.
  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 InceptionResNet[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。

4. 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。

  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 to be viable in practice, it must provide an acceptable model complexity and computational overhead which is important for scalability. To illustrate the cost of the module, we take the comparison between ResNet-50 and SE-ResNet-50 as an example, where the accuracy of SE-ResNet-50 is obviously superior to ResNet-50 and approaching a deeper ResNet-101 network (shown in Table 2). ResNet-50 requires ∼∼3.86 GFLOPs in a single forward pass for a 224×224224× 224 pixel input image. Each SE block makes use of a global average pooling operation in the squeeze phase and two small fully connected layers in the excitation phase, followed by an inexpensive channel-wise scaling operation. In aggregate, SE-ResNet-50 requires ∼∼3.87 GFLOPs, corresponding to only a 0.26%0.26% relative increase over the original ResNet-50.
  在实践中提出的 SE 块是可行的，它必须提供可接受的模型复杂 度和计算开销，这对于可伸缩性是重要的。为了说明模块的成本，作 为例子我们比较了 ResNet-50 和 SE-ResNet-50，其中 SE-ResNet-50 的 精确度明显优于 ResNet-50，接近更深的 ResNet-101 网络（如表 2 所 示）。对于 224×224224×224 像素的输入图像，ResNet-50 单次前向 传播需要∼∼ 3.86 GFLOP。每个 SE 块利用压缩阶段的全局平均池化 操作和激励阶段中的两个小的全连接层，接下来是廉价的通道缩放操 作。总的来说，SE-ResNet-50 需要∼∼ 3.87 GFLOP，相对于原始的 ResNet-50 只相对增加了 0.26%0.26%。

  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 256256 images, a single pass forwards and backwards through ResNet-50 takes 190190ms, compared to 209209ms 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 224×224224 × 224pixel input image, ResNet-50 takes 164164ms, compared to for SE-ResNet-5050. 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 190190 ms，而 SE-ResNet-50 则花费 209209ms（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 224×224224×224 像素的输入图像，ResNet-50 花费了 164164ms，相比之下，SE-ResNet-5050 花费了 167167ms。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: 2r∑s=1SNs⋅Cs22r∑s=1SNs⋅Cs2 where rr denotes the reduction ratio (we set rr to 1616 in all our experiments), SS refers to the number of stages (where each stage refers to the collection of blocks operating on feature maps of a common spatial dimension), CsCs denotes the dimension of the output channels for stage ss and NsNs refers to the repeated block number. In total, SEResNet-50 introduces ∼∼2.5 million additional parameters beyond the ∼∼25 million parameters required by ResNet-50, corresponding to a ∼10%∼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%<0.1%top-1 error on ImageNet dataset) to reduce the relative parameter increase to ∼4%∼4%, which may prove useful in cases where parameter usage is a key consideration.
  接下来，我们考虑所提出的块引入的附加参数。所有附加参数都 包含在门机制的两个全连接层中，构成网络总容量的一小部分。更确 切地说，引入的附加参数的数量由下式给出： 2r∑s=1SNs⋅Cs22r∑s=1SNs⋅Cs2 其中 rr 表示减少比率（我们在所有的实验中将 rr 设置为 1616）， SS 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），CsCs 表示阶段 ss 的输出通道的维度，NsNs 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%∼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%<0.1%的top-1 错误率）上被移除，将相对参数增加减少到∼4%∼4%，这在参 数使用是关键考虑的情况下可能证明是有用的。

5. Implementation 实现

  During training, we follow standard practice and perform data augmentation with random-size cropping [39] to 224×224224×224 pixels (299×299299 × 299 for Inception-ResNet-v2 [38] and SE-InceptionResNet-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 subbatches 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 224×224224×224 像素（299×299299×299 用于 Inception-ResNetv2[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。

6 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。

6.1 ImageNet Classification ImageNet 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 224×224224×224 pixels are cropped from each image whose shorter edge is first resized to 256 (299×299299×299 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 224×224224×224 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 299×299299×299 个像素）。
  Network depth. We first compare the SE-ResNet against a collection of standard ResNet architectures. Each ResNet and its corresponding SEResNet 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%6.62%, exceeding ResNet-50 (7.48%7.48%) by 0.86%0.86% and approaching the performance achieved by the much deeper ResNet-101 network (6.52%6.52% top-5 error) with only half of the computational overhead (3.873.87 GFLOPs vs. 7.587.58 GFLOPs). This pattern is repeated at greater depth, where SE-ResNet-101 (6.07%6.07% top-55 error) not only matches, but outperforms the deeper ResNet-152 network (6.34%6.34% top-5 error) by 0.27%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%6.62%的 top-5 验证错误率，超过了 ResNet-50（7.48%7.48%）0.86%0.86%，接 近更深的 ResNet-101 网络（6.52%6.52%的 top-5 错误率），且只有 ResNet-101 一半的计算开销（3.873.87 GFLOPs vs. 7.587.58 GFLOPs）。 这种模式在更大的深度上重复，SE-ResNet-101（6.07%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%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。

  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 (32×4d32×4d) 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（32×4d32×4d）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 a top-5 error of 5.49%5.49% which is superior to both its direct counterpart ResNeXt50 (5.90%5.90% top-5 error) as well as the deeper ResNeXt-101 (5.57%5.57% top-5 error), a model which has almost double the number of parameters and computational overhead. As for the experiments of Inception-ResNet-v2, we conjecture the difference of cropping strategy might lead to the gap between their reported result and our re-implemented one, as their original image size has not been clarified in [38] while we crop the 299×299299×299 region from a relative larger image (where the shorter edge is resized to 352). SE-Inception-ResNet-v2 (4.79%4.79% top5 error) outperforms our reimplemented Inception-ResNet-v2 (5.21%5.21% top-5 error) by 0.42%0.42% (a relative improvement of 8.1%8.1%) as well as the reported result in [38]. The optimisation curves for each network are depicted in Fig. 5, illustrating the consistency of the improvement yielded by SE blocks throughout the training process.
  表 2 中给出的结果说明在将 SE 块引入到两种架构中会引起显著 的性能改善。尤其是 SE-ResNeXt-50 的 top-5 错误率是 5.49%5.49%， 优于于它直接对应的 ResNeXt-50（5.90%5.90%的 top-5 错误率）以及 更深的 ResNeXt-101（5.57%5.57%的 top-5 错误率），这个模型几乎 有两倍的参数和计算开销。对于 Inception-ResNet-v2 的实验，我们猜 测可能是裁剪策略的差异导致了其报告结果与我们重新实现的结果 之间的差距，因为它们的原始图像大小尚未在[38]中澄清，而我们从 相对较大的图像（其中较短边被归一化为 352）中裁剪出 299×299299 ×299 大小的区域。SE-Inception-ResNet-v2（4.79%4.79%的 top-5 错 误率）比我们重新实现的 Inception-ResNet-v2（5.21%5.21%的 top-5 错 误率）要低 0.42%0.42%（相对改进了 8.1%8.1%）也优于[38]中报告 的结果。每个网络的优化曲线如图 5 所示，说明了在整个训练过程中 SE 块产生了一致的改进。
  Finally, we assess the effect of SE blocks when operating on a nonresidual network by conducting experiments with the BN-Inception architecture [14] which provides good performance at a lower model complexity. The results of the comparison are shown in Table 2 and the training curves are shown in Fig. 6, exhibiting the same phenomena that emerged in the residual architectures. In particular, SE-BN-Inception achieves a lower top-5 error of 7.14%7.14% in comparison to BNInception whose error rate is 7.89%7.89%. These experiments demonstrate that improvements induced by SE blocks can be used in combination with a wide range of architectures. Moreover, this result holds for both residual and non-residual foundations.
  最后，我们通过对 BN-Inception 架构[14]进行实验来评估 SE 块 在非残差网络上的效果，该架构在较低的模型复杂度下提供了良好的 性能。比较结果如表 2 所示，训练曲线如图 6 所示，表现出的现象与 残差架构中出现的现象一样。尤其是与 BN-Inception 7.89%7.89%的错 误率相比，SE-BN-Inception 获得了更低 7.14%7.14%的 top-5 错误。 这些实验表明 SE 块引起的改进可以与多种架构结合使用。而且，这个结果适用于残差和非残差基础。

  Results on ILSVRC 2017 Classification Competition. ILSVRC [30] is an annual computer vision competition which has proved to be a fertile ground for model developments in image classification. The training and validation data of the ILSVRC 2017 classification task are drawn from the ImageNet 2012 dataset, while the test set consists of an additional unlabelled 100K images. For the purposes of the competition, the top-5 error metric is used to rank entries.
  ILSVRC 2017 分类竞赛的结果。ILSVRC[30]是一个年度计算机 视觉竞赛，被证明是图像分类模型发展的沃土。ILSVRC 2017 分类任 务的训练和验证数据来自 ImageNet 2012 数据集，而测试集包含额外 的未标记的 10 万张图像。为了竞争的目的，使用 top-5 错误率度量来 对输入条目进行排序。
  SENets formed the foundation of our submission to the challenge where we won first place. Our winning entry comprised a small ensemble of SENets that employed a standard multi-scale and multi-crop fusion strategy to obtain a 2.251%2.251% top-5 error on the test set. This result represents a ∼25%∼25% relative improvement on the winning entry of 2016 (2.99%2.99% top-5 error). One of our high-performing networks is constructed by integrating SE blocks with a modified ResNeXt [43] (details of the modifications are provided in Appendix A). We compare the proposed architecture with the state-of-the-art models on the ImageNet validation set in Table 3. Our model achieves a top-1 error of 18.68%18.68% and a top-5 error of 4.47%4.47% using a 224×224224 ×224 centre crop evaluation on each image (where the shorter edge is first resized to 256). To enable a fair comparison with previous models, we also provide a 320×320320×320 centre crop evaluation, obtaining the lowest error rate under both the top-1 (17.28%17.28%) and the top-5 (3.79%3.79%) error metrics.
  SENets 是我们在挑战中赢得第一名的基础。我们的获胜输入由 一小群 SENets 组成，它们采用标准的多尺度和多裁剪图像融合策略， 在测试集上获得了 2.251%2.251%的 top-5 错误率。这个结果表示在 2016 年获胜输入（2.99%2.99%的 top-5 错误率）的基础上相对改进了 ∼25%∼25% 。我们 的 高 性 能网 络之 一 是将 SE 块与 修 改 后 的 ResNeXt[43]集成在一起构建的（附录 A 提供了这些修改的细节）。 在表 3 中我们将提出的架构与最新的模型在 ImageNet 验证集上进行 了比较。我们的模型在每一张图像使用 224×224224×224 中间裁剪评 估（短边首先归一化到 256）取得了 18.68%18.68%的 top-1 错误率和 4.47%4.47%的 top-5 错误率。为了与以前的模型进行公平的比较，我 们也提供了 320×320320×320 的中心裁剪图像评估，在 top- 1(17.28%17.28%)和 top-5(3.79%3.79%)的错误率度量中获得了最低的 错误率。

6.2 Scene Classification 场景分类

  Large portions of the ImageNet dataset consist of images dominated by single objects. To evaluate our proposed model in more diverse scenarios, we also evaluate it on the Places365-Challenge dataset [48] for scene classification. This dataset comprises 8 million training images and 36, 500 validation images across 365 categories. Relative to classification, the task of scene understanding can provide a better assessment of the ability of a model to generalise well and handle abstraction, since it requires the capture of more complex data associations and robustness to a greater level of appearance variation.
  ImageNet 数据集的大部分由单个对象支配的图像组成。为了在 更多不同的场景下评估我们提出的模型，我们还在 Places365- Challenge 数据集[48]上对场景分类进行评估。该数据集包含 800 万张 训练图像和 365 个类别的 36500 张验证图像。相对于分类，场景理解 的任务可以更好地评估模型泛化和处理抽象的能力，因为它需要捕获 更复杂的数据关联以及对更大程度外观变化的鲁棒性。
  We use ResNet-152 as a strong baseline to assess the effectiveness of SE blocks and follow the evaluation protocol in [33]. Table 4 shows the results of training a ResNet-152 model and a SE-ResNet-152 for the given task. Specifically, SE-ResNet-152 (11.01%11.01%top-5 error) achieves a lower validation error than ResNet-152 (11.61%11.61% top-5 error), providing evidence that SE blocks can perform well on different datasets. This SENet surpasses the previous state-of-the-art model Places-365-CNN [33] which has a top-5 error of 11.48%11.48% on this task.
  我们使用 ResNet-152 作为强大的基线来评估 SE 块的有效性，并 遵循[33]中的评估协议。表 4 显示了针对给定任务训练 ResNet-152 模 型和 SE-ResNet-152 的结果。具体而言，SE-ResNet-152（11.01%11.01% 的 top-5 错误率）取得了比 ResNet-152（11.61%11.61%的 top-5 错误 率）更低的验证错误率，证明了 SE 块可以在不同的数据集上表现良 好。这个 SENet 超过了先前的最先进的模型 Places-365-CNN [33]，它 在这个任务上有 11.48%11.48%的 top-5 错误率。

6.3 Analysis and Discussion Reduction ratio 分析和讨论 减少比率

  The reduction ratio rr introduced in Eqn. (5) is an important hyperparameter which allows us to vary the capacity and computational cost of the SE blocks in the model. To investigate this relationship, we conduct experiments based on the SE-ResNet-50 architecture for a range of different rr values. The comparison in Table 5 reveals that performance does not improve monotonically with increased capacity. This is likely to be a result of enabling the SE block to overfit the channel interdependencies of the training set. In particular, we found that setting r=16r=16 achieved a good tradeoff between accuracy and complexity and consequently, we used this value for all experiments.
  公式（5）中引入的减少比率 rr 是一个重要的超参数， 它允许我们改变模型中 SE 块的容量和计算成本。为了研究这种关系， 我们基于 SE-ResNet-50 架构进行了一系列不同 rr 值的实验。表 5 中 的比较表明，性能并没有随着容量的增加而单调上升。这可能是使 SE 块能够过度拟合训练集通道依赖性的结果。尤其是我们发现设置 r=16r=16 在精度和复杂度之间取得了很好的平衡，因此我们将这个值 用于所有的实验。

  The role of Excitation. While SE blocks have been empirically shown to improve network performance, we would also like to understand how the self-gating excitation mechanism operates in practice. To provide a clearer picture of the behaviour of SE blocks, in this section we study example activations from the SE-ResNet-50 model and examine their distribution with respect to different classes at different blocks. Specifically, we sample four classes from the ImageNet dataset that exhibit semantic and appearance diversity, namely goldfish, pug, plane and cliff (example images from these classes are shown in Fig. 7). We then draw fifty samples for each class from the validation set and compute the average activations for fifty uniformly sampled channels in the last SE block in each stage (immediately prior to downsampling) and plot their distribution in Fig. 8. For reference, we also plot the distribution of average activations across all 1000 classes.
  激励的作用。虽然 SE 块从经验上显示出其可以改善网络性能， 但我们也想了解自门激励机制在实践中是如何运作的。为了更清楚地 描述 SE 块的行为，本节我们研究 SE-ResNet-50 模型的样本激活，并 考察它们在不同块不同类别下的分布情况。具体而言，我们从 ImageNet 数据集中抽取了四个类，这些类表现出语义和外观多样性， 即金鱼，哈巴狗，刨和悬崖（图 7 中显示了这些类别的示例图像）。 然后，我们从验证集中为每个类抽取 50 个样本，并计算每个阶段最 后的 SE 块中 50 个均匀采样通道的平均激活（紧接在下采样之前）， 并在图 8 中绘制它们的分布。作为参考，我们也绘制所有 1000 个类 的平均激活分布。

  We make the following three observations about the role of Excitation in SENets. First, the distribution across different classes is nearly identical in lower layers, e.g. SE_2_3. This suggests that the importance of feature channels is likely to be shared by different classes in the early stages of the network. Interestingly however, the second observation is that at greater depth, the value of each channel becomes much more class-specific as different classes exhibit different preferences to the discriminative value of features e.g. SE_4_6 and SE_5_1. The two observations are consistent with findings in previous work [21, 46], namely that lower layer features are typically more general (i.e. class agnostic in the context of classification) while higher layer features have greater specificity. As a result, representation learning benefits from the recalibration induced by SE blocks which adaptively facilitates feature extraction and specialisation to the extent that it is needed. Finally, we observe a somewhat different phenomena in the last stage of the network. SE_5_2 exhibits an interesting tendency towards a saturated state in which most of the activations are close to 1 and the remainder are close to 0. At the point at which all activations take the value 1, this block would become a standard residual block. At the end of the network in the SE_5_3 (which is immediately followed by global pooling prior before classifiers), a similar pattern emerges over different classes, up to a slight change in scale (which could be tuned by the classifiers). This suggests that SE_5_2 and SE_5_3 are less important than previous blocks in providing recalibration to the network. This finding is consistent with the result of the empirical investigation in Sec. 4 which demonstrated that the overall parameter count could be significantly reduced by removing the SE blocks for the last stage with only a marginal loss of performance (< 0.1%0.1% top-1 error).
  我们对 SENets 中 Excitation 的作用提出以下三点看法。首先，不 同类别的分布在较低层中几乎相同，例如，SE_2_3。这表明在网络的 最初阶段特征通道的重要性很可能由不同的类别共享。然而有趣的是， 第二个观察结果是在更大的深度，每个通道的值变得更具类别特定性， 因为不同类别对特征的判别性值具有不同的偏好。SE_4_6 和SE_5_1。 这两个观察结果与以前的研究结果一致[21,46]，即低层特征通常更普 遍（即分类中不可知的类别），而高层特征具有更高的特异性。因此， 表示学习从 SE 块引起的重新校准中受益，其自适应地促进特征提取 和专业化到所需要的程度。最后，我们在网络的最后阶段观察到一个 有些不同的现象。SE_5_2 呈现出朝向饱和状态的有趣趋势，其中大 部分激活接近于 1，其余激活接近于 0。在所有激活值取 1 的点处， 该块将成为标准残差块。在网络的末端 SE_5_3 中（在分类器之前紧 接着是全局池化），类似的模式出现在不同的类别上，尺度上只有轻 微的变化（可以通过分类器来调整）。这表明，SE_5_2 和 SE_5_3 在 为网络提供重新校准方面比前面的块更不重要。这一发现与第四节实 证研究的结果是一致的，这表明，通过删除最后一个阶段的 SE 块， 总体参数数量可以显著减少，性能只有一点损失（<0.1%0.1%的 top1 错误率）

7. Conclusion 结论

   In this paper we proposed the SE block, a novel architectural unit designed to improve the representational capacity of a network by enabling it to perform dynamic channel-wise feature recalibration. Extensive experiments demonstrate the effectiveness of SENets which achieve stateof-the-art performance on multiple datasets. In addition, they provide some insight into the limitations of previous architectures in modelling channelwise feature dependencies, which we hope may prove useful for other tasks requiring strong discriminative features. Finally, the feature importance induced by SE blocks may be helpful to related fields such as network pruning for compression.
  在本文中，我们提出了 SE 块，这是一种新颖的架构单元，旨在 通过使网络能够执行动态通道特征重新校准来提高网络的表示能力。 大量实验证明了 SENets 的有效性，其在多个数据集上取得了最先进 的性能。此外，它们还提供了一些关于以前架构在建模通道特征依赖 性上的局限性的洞察，我们希望可能证明 SENets 对其它需要强判别 性特征的任务是有用的。最后，由 SE 块引起的特征重要性可能有助 于相关领域，例如为了压缩的网络修剪。