论文标题
基于相似性的聚类，用于增强图像分类体系结构
Similarity-Based Clustering for Enhancing Image Classification Architectures
论文作者
论文摘要
卷积网络是一流的计算机视觉应用程序的中心，用于各种承诺。自2014年以来，大量的工作开始使更好的卷积体系结构在不同的基准中产生了慷慨的补充。尽管扩大模型尺寸和计算成本通常会在大多数工作中平均及时提高质量，但是现在，架构现在需要提供一些其他信息以提高性能。我表明的证据表明，通过基于内容的图像相似性和深度学习模型的合并，我们可以提供可用于使聚类学习成为可能的信息流。该论文显示了子数据集群集的培训不仅如何降低计算成本，而且还会提高评估和调整给定数据集对模型的速度。
Convolutional networks are at the center of best-in-class computer vision applications for a wide assortment of undertakings. Since 2014, a 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. I show 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. The paper shows how training of sub-dataset clusters not only reduces the cost of computation but also increases the speed of evaluating and tuning a model on the given dataset.
