论文标题
有效的CNN，带有不相关的功能袋
Efficient CNN with uncorrelated Bag of Features pooling
论文作者
论文摘要
尽管CNN的性能出色，但将它们部署在低计算功率设备上仍然有限，因为它们通常在计算上昂贵。高复杂性的一个关键原因是卷积层与完全连接的层之间的连接，这通常需要大量参数。为了减轻这个问题，最近提出了一系列功能（BOF）合并。 BOF学习了一个字典，该字典用于编译输入的直方图表示。在本文中，我们提出了一种基于BOF Poling之上的方法来提高其效率，以确保学习词典的项目不是冗余的。我们根据字典的项目的成对相关性提出了一个额外的损失项，该词典的相关性补充了标准损失，以明确规范模型以学习更多样化和丰富的词典。提出的策略产生了BOF的有效变体，并进一步提高了其性能，而无需任何其他参数。
Despite the superior performance of CNN, deploying them on low computational power devices is still limited as they are typically computationally expensive. One key cause of the high complexity is the connection between the convolution layers and the fully connected layers, which typically requires a high number of parameters. To alleviate this issue, Bag of Features (BoF) pooling has been recently proposed. BoF learns a dictionary, that is used to compile a histogram representation of the input. In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant. We propose an additional loss term, based on the pair-wise correlation of the items of the dictionary, which complements the standard loss to explicitly regularize the model to learn a more diverse and rich dictionary. The proposed strategy yields an efficient variant of BoF and further boosts its performance, without any additional parameters.
