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In this paper, a dynamic niching clustering algorithm based on individual-connectedness (DNIC) is proposed for unsupervised classification with no prior knowledge. It aims to automatically evolve the optimal number of clusters as well as the cluster centers of the data set based on the proposed adaptive compact k-distance neighborhood algorithm. More specifically, with the adaptive selection of the...
Recently, deep convolutional neural networks (DCNNs) have significantly promoted the development of semantic image segmentation. However, previous works on learning the segmentation network often rely on a large number of ground-truths with pixel-level annotations, which usually require considerable human effort. In this paper, we explore a more challenging problem by learning to segment under image-level...
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