基于深度学习的多标签生成研究进展

大数据时代,数据呈现维度高、数据量大和增长快等特点。如何有效利用其中蕴含的有价值信息,以实现数据的智能化处理,已成为当前理论和应用的研究热点。针对现实普遍存在的多义性对象,数据多标签被提出并被广泛应用于数据智能化组织。近年来,深度学习在数据特征提取方面呈现出高速、高精度等优异性,使基于深度学习的多标签生成得到广泛关注。文中分五大类别总结了最新研究成果,并进一步从数据、关系类型、应用场景、适应性及实验性能方面对其进行对比和分析,最后探讨了多标签生成面临的挑战和未来的研究方向。

Research and Development of Multi-label Generation Based on Deep Learning

In the era of big data,data show the characteristics of high dimension,large amount and rapid growth.Efficiently discovering knowledge from these data is a research focus.Multi-label has been proposed for ambiguous objects in reality,and is widely used in data intelligent processing.In recent years,Multi-label generation receives widespread attention due to the excellent performance of deep learning.The latest research results were summarized from five categories and were further compared and analyzed from the aspects of data,relationship types,application scene,adaptability and experimental performance.Finally,the challenges of multi-label generation were discussed,followed with the prospects for future work.