现代卷积神经网络（CNNS）具有大规模的相同卷积块，因此，已经提出了跨越这些块的参数的递归共享，以减少参数的数量。然而，Naive的参数共享构成了许多挑战，例如有限的代表性功率和递归参数的消失/爆炸梯度问题。在本文中，我们提出了一种递归卷积块设计和训练方法，其中分离和学习递归和基于递归部分或过滤器的基础，同时在训练期间有效地避免消失/爆炸梯度问题。我们表明，可以通过强制执行滤波器基础的元素来控制笨拙的消失/爆炸梯度问题，并且经验证明所提出的正交正规化在训练期间提高了梯度的流动。实验结果对图像分类和对象检测表明，我们的方法与以前的参数共享方法不同，不交易性能，以节省参数，并始终如一地优于聚参数化的对应网络。这种卓越的性能表明，所提出的递归卷积块设计和正交正常化不仅可以防止性能下降，而且还一致地提高表示能力，而递归地共享大量参数。

由于存储器和计算资源有限，部署在移动设备上的卷积神经网络（CNNS）是困难的。我们的目标是通过利用特征图中的冗余来设计包括CPU和GPU的异构设备的高效神经网络，这很少在神经结构设计中进行了研究。对于类似CPU的设备，我们提出了一种新颖的CPU高效的Ghost（C-Ghost）模块，以生成从廉价操作的更多特征映射。基于一组内在的特征映射，我们使用廉价的成本应用一系列线性变换，以生成许多幽灵特征图，可以完全揭示内在特征的信息。所提出的C-Ghost模块可以作为即插即用组件，以升级现有的卷积神经网络。 C-Ghost瓶颈旨在堆叠C-Ghost模块，然后可以轻松建立轻量级的C-Ghostnet。我们进一步考虑GPU设备的有效网络。在建筑阶段的情况下，不涉及太多的GPU效率（例如，深度明智的卷积），我们建议利用阶段明智的特征冗余来制定GPU高效的幽灵（G-GHOST）阶段结构。舞台中的特征被分成两个部分，其中使用具有较少输出通道的原始块处理第一部分，用于生成内在特征，另一个通过利用阶段明智的冗余来生成廉价的操作。在基准测试上进行的实验证明了所提出的C-Ghost模块和G-Ghost阶段的有效性。 C-Ghostnet和G-Ghostnet分别可以分别实现CPU和GPU的准确性和延迟的最佳权衡。代码可在https://github.com/huawei-noah/cv-backbones获得。

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight Ghost-Net can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https: //github.com/huawei-noah/ghostnet.

Low-rankness plays an important role in traditional machine learning, but is not so popular in deep learning. Most previous low-rank network compression methods compress the networks by approximating pre-trained models and re-training. However, the optimal solution in the Euclidean space may be quite different from the one in the low-rank manifold. A well-pre-trained model is not a good initialization for the model with low-rank constraints. Thus, the performance of a low-rank compressed network degrades significantly. Compared to other network compression methods such as pruning, low-rank methods attracts less attention in recent years. In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance. First, we propose to alternately perform stochastic gradient descent training and projection onto the low-rank manifold. Compared to re-training on the compact model, this enables full utilization of model capacity since solution space is relaxed back to Euclidean space after projection. Second, the matrix energy (the sum of squares of singular values) reduction caused by projection is compensated by energy transfer. We uniformly transfer the energy of the pruned singular values to the remaining ones. We theoretically show that energy transfer eases the trend of gradient vanishing caused by projection. Third, we propose batch normalization (BN) rectification to cut off its effect on the optimal low-rank approximation of the weight matrix, which further improves the performance. Comprehensive experiments on CIFAR-10 and ImageNet have justified that our method is superior to other low-rank compression methods and also outperforms recent state-of-the-art pruning methods. Our code is available at https://github.com/BZQLin/LRPET.

我们提出了一种多移民通道（MGIC）方法，该方法可以解决参数数量相对于标准卷积神经网络（CNN）中的通道数的二次增长。因此，我们的方法解决了CNN中的冗余，这也被轻量级CNN的成功所揭示。轻巧的CNN可以达到与参数较少的标准CNN的可比精度。但是，权重的数量仍然随CNN的宽度四倍地缩放。我们的MGIC体系结构用MGIC对应物代替了每个CNN块，该块利用了小组大小的嵌套分组卷积的层次结构来解决此问题。因此，我们提出的架构相对于网络的宽度线性扩展，同时保留了通道的完整耦合，如标准CNN中。我们对图像分类，分割和点云分类进行的广泛实验表明，将此策略应用于Resnet和MobilenetV3等不同体系结构，可以减少参数的数量，同时获得相似或更好的准确性。

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark datasets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and predictive performance.

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on Ima-geNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ∼13× actual speedup over AlexNet while maintaining comparable accuracy.

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layerwise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layerdeep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https: //github.com/szagoruyko/wide-residual-networks.

Adder神经网络（Addernets）在图像分类上表现出令人印象深刻的性能，只有加法操作，比使用乘法建立的传统卷积神经网络更节能。与分类相比，对通过Addernets降低现代对象探测器的能耗的强烈需求，例如自主驾驶和面部检测。在本文中，我们提出了对物体检测的addernets的实证研究。我们首先揭示了预先训练的加法器骨架中的批量归一化统计，不应冻结，因为Addernets的相对较大的特征方差。此外，我们在颈部中插入更多的快捷方式连接，并设计一个新的特征融合架构，以避免加法器层的稀疏功能。我们展示了广泛的消融研究，探讨了加法器探测器的几种设计选择。与最先进的比较在Coco和Pascal VOC基准上进行。具体而言，所提出的加法器FCOS在Coco Val集上实现了37.8 \％AP，展示了卷积对应物的相当性能，具有约1.4倍的能量减少。

The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20× reduction in model size and a 5× reduction in computing operations.

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity.To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local crosschannel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.

由于稀疏神经网络通常包含许多零权重，因此可以在不降低网络性能的情况下潜在地消除这些不必要的网络连接。因此，设计良好的稀疏神经网络具有显着降低拖鞋和计算资源的潜力。在这项工作中，我们提出了一种新的自动修剪方法 - 稀疏连接学习（SCL）。具体地，重量被重新参数化为可培训权重变量和二进制掩模的元素方向乘法。因此，由二进制掩模完全描述网络连接，其由单位步进函数调制。理论上，从理论上证明了使用直通估计器（STE）进行网络修剪的基本原理。这一原则是STE的代理梯度应该是积极的，确保掩模变量在其最小值处收敛。在找到泄漏的Relu后，SoftPlus和Identity Stes可以满足这个原理，我们建议采用SCL的身份STE以进行离散面膜松弛。我们发现不同特征的面具梯度非常不平衡，因此，我们建议将每个特征的掩模梯度标准化以优化掩码变量训练。为了自动训练稀疏掩码，我们将网络连接总数作为我们的客观函数中的正则化术语。由于SCL不需要由网络层设计人员定义的修剪标准或超级参数，因此在更大的假设空间中探讨了网络，以实现最佳性能的优化稀疏连接。 SCL克服了现有自动修剪方法的局限性。实验结果表明，SCL可以自动学习并选择各种基线网络结构的重要网络连接。 SCL培训的深度学习模型以稀疏性，精度和减少脚波特的SOTA人类设计和自动修剪方法训练。

深度学习技术在各种任务中都表现出了出色的有效性，并且深度学习具有推进多种应用程序（包括在边缘计算中）的潜力，其中将深层模型部署在边缘设备上，以实现即时的数据处理和响应。一个关键的挑战是，虽然深层模型的应用通常会产生大量的内存和计算成本，但Edge设备通常只提供非常有限的存储和计算功能，这些功能可能会在各个设备之间差异很大。这些特征使得难以构建深度学习解决方案，以释放边缘设备的潜力，同时遵守其约束。应对这一挑战的一种有希望的方法是自动化有效的深度学习模型的设计，这些模型轻巧，仅需少量存储，并且仅产生低计算开销。该调查提供了针对边缘计算的深度学习模型设计自动化技术的全面覆盖。它提供了关键指标的概述和比较，这些指标通常用于量化模型在有效性，轻度和计算成本方面的水平。然后，该调查涵盖了深层设计自动化技术的三类最新技术：自动化神经体系结构搜索，自动化模型压缩以及联合自动化设计和压缩。最后，调查涵盖了未来研究的开放问题和方向。

在每个卷积层中学习一个静态卷积内核是现代卷积神经网络（CNN）的常见训练范式。取而代之的是，动态卷积的最新研究表明，学习$ n $卷积核与输入依赖性注意的线性组合可以显着提高轻重量CNN的准确性，同时保持有效的推断。但是，我们观察到现有的作品endow卷积内核具有通过一个维度（关于卷积内核编号）的动态属性（关于内核空间的卷积内核编号），但其他三个维度（关于空间大小，输入通道号和输出通道编号和输出通道号，每个卷积内核）被忽略。受到这一点的启发，我们提出了Omni维动态卷积（ODCONV），这是一种更普遍而优雅的动态卷积设计，以推进这一研究。 ODCONV利用了一种新型的多维注意机制，采用平行策略来学习沿着任何卷积层的内核空间的所有四个维度学习卷积内核的互补关注。作为定期卷积的倒数替换，可以将ODCONV插入许多CNN架构中。 ImageNet和MS-Coco数据集的广泛实验表明，ODCONV为包括轻量重量和大型的各种盛行的CNN主链带来了可靠的准确性提升，例如3.77％〜5.71％| 1.86％〜3.72％〜3.72％的绝对1个绝对1改进至ImabivLenetV2 | ImageNet数据集上的重新连接家族。有趣的是，由于其功能学习能力的提高，即使具有一个单个内核的ODCONV也可以与具有多个内核的现有动态卷积对应物竞争或超越现有的动态卷积对应物，从而大大降低了额外的参数。此外，ODCONV也优于其他注意模块，用于调节输出特征或卷积重量。

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability. To address this issue, we present Dynamic Convolution, a new design that increases model complexity without increasing the network depth or width. Instead of using a single convolution kernel per layer, dynamic convolution aggregates multiple parallel convolution kernels dynamically based upon their attentions, which are input dependent. Assembling multiple kernels is not only computationally efficient due to the small kernel size, but also has more representation power since these kernels are aggregated in a non-linear way via attention. By simply using dynamic convolution for the state-ofthe-art architecture MobileNetV3-Small, the top-1 accuracy of ImageNet classification is boosted by 2.9% with only 4% additional FLOPs and 2.9 AP gain is achieved on COCO keypoint detection.

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections-one between each layer and its subsequent layer-our network has L(L+1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.

The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or to impose a hyperparameter-sensitive sparse constraint on the loss objective to regularize the network training. In this paper, we present a novel filter pruning method, dubbed dynamic-coded filter fusion (DCFF), to derive compact CNNs in a computation-economical and regularization-free manner for efficient image classification. Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance. In contrast to simply keeping high-score filters in other methods, we propose the concept of filter fusion, i.e., the weighted averages using the assigned proxies, as our preserved filters. We obtain a one-hot inter-similarity distribution as the temperature parameter approaches infinity. Thus, the relative importance of each filter can vary along with the training of the compact CNN, leading to dynamically changeable fused filters without both the dependency on the pretrained model and the introduction of sparse constraints. Extensive experiments on classification benchmarks demonstrate the superiority of our DCFF over the compared counterparts. For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10. A compact ResNet-50 is obtained with 63.8% FLOPs and 58.6% parameter reductions, retaining 75.60% top-1 accuracy on ILSVRC-2012. Our code, narrower models and training logs are available at https://github.com/lmbxmu/DCFF.

在过去几年中，神经网络的性能在越来越多的浮点操作（拖鞋）的成本上显着提高。但是，当计算资源有限时，更多的拖鞋可能是一个问题。作为解决这个问题的尝试，修剪过滤器是一种常见的解决方案，但大多数现有的修剪方法不有效地保持模型精度，因此需要大量的芬降时期。在本文中，我们提出了一种自动修剪方法，该方法学习保存的神经元以保持模型精度，同时将絮凝到预定目标。为了完成这项任务，我们介绍了一种可训练的瓶颈，只需要一个单一的单一时期，只需要一个数据集的25.6％（Cifar-10）或7.49％（ILSVRC2012）来了解哪些过滤器。在各种架构和数据集上的实验表明，该方法不仅可以在修剪后保持精度，而且在FineTuning之后也优越现有方法。我们在Reset-50上达到了52.00％的拖鞋，在ILSVRC2012上的灌溉后的前1个精度为47.51％，最先进的（SOTA）精度为76.63％。代码可用（链接匿名审核）。

最近的各向同性网络，例如Convmixer和Vision Transformers，在视觉识别任务中发现了巨大的成功，匹配或胜过非方向性卷积神经网络（CNNS）。各向同性架构特别适合跨层重量共享，这是一种有效的神经网络压缩技术。在本文中，我们对各向同性网络中共享参数的方法（SPIN）进行了经验评估。我们提出了一个框架，以形式化重量分享设计决策并对此设计空间进行全面的经验评估。在我们的实验结果的指导下，我们提出了一种重量共享策略，以与仅传统缩放方法相比，在拖放和参数与准确性方面，产生一个具有更好总体效率的模型家族，例如，将Convmixer压缩为1.9倍，同时提高准确性的准确性成像网。最后，我们进行定性研究，以进一步了解各向同性体系结构中的重量共享的行为。该代码可在https://github.com/apple/ml-pin上找到。