Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required time and space overheads so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the technical challenges faced by future developments of ensemble learning under the era of deep learning.
翻译:由于在各种人工智能应用中具有深层次学习(主要是深层神经网络)的主导地位,最近,基于深层神经网络的全方位学习(整体深层学习)显示出在改进学习系统一般化方面的显著表现,然而,由于现代深层神经网络通常有数百万至数十亿参数,培训多基深层学习者和与全深层学习者进行测试的时间和空间间接费用远远大于传统共同学习的优势。虽然已提出一些快速混合深层学习的算法,以促进在某些应用中部署共同深层学习,但对于特定领域的许多应用,仍然需要取得进一步进展,因为在这些领域中,开发的时间和计算资源通常受到限制,或处理的数据具有很大的多维度。一个紧迫的问题需要解决的是,如何利用堆积深层学习的巨大优势,同时减少必要的时间和空间管理,这样,在具体领域的更多应用将从中受益。为了缓解这一问题,我们必须了解在深层的深度学习过程中,在最新技术学习方法的时代,我们必须了解如何深入地学习,我们如何深入了解当前学习的学习,在这种深层次的学习方法中,我们必须了解最新学习的学习成果的学习成果。