feature maps
时间: 2023-09-14 08:03:46 浏览: 44
Feature maps are the output of convolutional layers in a neural network. They are a set of two-dimensional arrays that represent the activation of each filter in the convolutional layer. Each feature map highlights a particular feature or pattern in the input data, and combining multiple feature maps allows the network to learn more complex representations of the input. Feature maps are typically used in computer vision tasks such as image classification, object detection, and segmentation.
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翻译这段英文,并解释: Deploying convolutional neural networks (CNNs) on em-bedded 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 de-sign. 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 convo-lutional 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-I accuracy) than MobileNetV3 with similar computational cost on the ImaseNet ILSVRC2012 classification dataset.
部署卷积神经网络(CNN)到嵌入式设备上是困难的,因为这些设备的内存和计算资源有限。特征图中的冗余是成功的CNN的一个重要特征,但在神经结构设计中很少被研究。本文提出了一种新颖的 Ghost 模块,可以通过廉价的操作生成更多的特征图。基于一组固有特征图,我们应用一系列廉价的线性变换来生成许多鬼特征图,这些特征图可以完全揭示固有特征之下的信息。所提出的 Ghost 模块可以作为插件式组件,升级现有的卷积神经网络。Ghost 瓶颈被设计为堆叠 Ghost 模块,然后可以轻松地建立轻量级 Ghost-Net。在基准测试中进行的实验表明,所提出的 Ghost 模块是基线模型中卷积层的一个令人印象深刻的替代品,我们的 GhostNet 在 ImaseNet ILSVRC2012 分类数据集上可以实现比 MobileNetV3 更高的识别性能(例如,75.7% 的 top-I 准确率),并且计算成本类似。
本文提出了一种新的 Ghost 模块,可以生成更多的特征图,以提高卷积神经网络的性能。Ghost 模块可以作为插件式组件,轻松地升级现有的卷积神经网络。GhostNet 通过堆叠 Ghost 模块,可以轻松地建立轻量级神经网络。实验表明,Ghost 模块是卷积层的一个令人印象深刻的替代品,GhostNet 可以实现比 MobileNetV3 更高的识别性能,但计算成本类似。
解释以下这段话The vectorized single-channel feature map of the base sample is denoted as a0 and the vectorized single-channel feature maps of base background samples are denoted as ai .
这段话是在描述一个算法或者方法中涉及的几个变量的含义。其中,这个算法或方法涉及到多个样本,包括一个基础样本(base sample)和多个背景样本(background samples)。每个样本中都包含一个特征图(feature map),这些特征图都是单通道的(single-channel)。特别地,基础样本的特征图用a0来表示,而背景样本的特征图用ai来表示,其中i表示背景样本的编号。这些特征图都是向量化的(vectorized),也就是说,被表示成一个一维的向量。通过对这些特征图进行处理,可以得到一些与这些样本相关的信息,进而用于后续的分析和决策。