This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the “Activate-and-Freeze” block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

中文翻译：

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具有智能决策机制的深度建模中的大规模数据驱动优化


本研究的重点是建立一种智能决策注意机制，其中特定深度密集ConvNet块之间的通道关系和行为特征图相互连接。因此，在深度建模中开发一种具有金字塔空间通道注意机制（FPSC-Net）的新型冻结网络。该模型研究大规模数据驱动的优化和创建过程中的具体设计选择如何影响所设计的深度智能模型的准确性和有效性之间的平衡。为此，本研究提出了一种新颖的架构单元，称为流行且竞争激烈的数据集上的“激活和冻结”块。为了通过在局部感受野内融合空间和通道信息来提取信息特征并提高表示能力，本研究构建了一个密集注意力模块（金字塔空间通道（PSC）注意力）来执行特征重新校准，并通过PSC 注意建模卷积特征通道之间的相互依赖关系。我们在激活和回冻结策略中加入 PSC 注意力模块，以搜索网络中最重要的部分之一进行提取和优化。在各种大规模数据集上的实验表明，与其他最先进的深度模型相比，所提出的方法在提高 ConvNets 表示能力方面可以实现更好的性能。