Convolutional networks are at the center of best in class computer vision applications for a wide assortment of undertakings. Since 2014, profound amount of work began to make better convolutional architectures, yielding generous additions in different benchmarks. Albeit expanded model size and computational cost will, in general, mean prompt quality increases for most undertakings but, the architectures now need to have some additional information to increase the performance. We show empirical evidence that with the amalgamation of content-based image similarity and deep learning models, we can provide the flow of information which can be used in making clustered learning possible. We show how parallel training of sub-dataset clusters not only reduces the cost of computation but also increases the benchmark accuracies by 5-11 percent.

中文翻译：

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用于增强图像分类架构的基于相似性的聚类

卷积网络处于同类最佳计算机视觉应用程序的中心，适用于各种各样的事业。自 2014 年以来，大量的工作开始制作更好的卷积架构，在不同的基准测试中产生了大量的补充。尽管扩展的模型大小和计算成本通常意味着大多数项目的质量会迅速提高，但架构现在需要一些额外的信息来提高性能。我们展示了经验证据，通过基于内容的图像相似性和深度学习模型的融合，我们可以提供可用于使聚类学习成为可能的信息流。我们展示了子数据集集群的并行训练如何不仅降低了计算成本，而且还将基准准确率提高了 5-11%。