Attention机制最早是在视觉图像领域提出来的,但是真正火起来应该算是google mind团队的这篇论文《Recurrent Models of Visual Attention》[14],他们在RNN模型上使用了attention机制来进行图像分类。随后,Bahdanau等人在论文《Neural Machine Translation by Jointly Learning to Align and Translate》 [1]中,使用类似attention的机制在机器翻译任务上将翻译和对齐同时进行,他们的工作算是是第一个提出attention机制应用到NLP领域中。接着类似的基于attention机制的RNN模型扩展开始应用到各种NLP任务中。最近,如何在CNN中使用attention机制也成为了大家的研究热点。下图表示了attention研究进展的大概趋势。

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注意力是一种在广泛的神经结构中使用的越来越流行的机制。由于这一领域的快速发展,仍然缺乏对注意力的系统概述。在本文中,我们定义了用于自然语言处理的注意力体系结构的统一模型,重点介绍了用于文本数据的向量表示的体系结构。我们讨论了以往工作的不同方面,注意力机制的可能用途,并描述了该领域的主要研究工作和公开挑战。

https://web.eecs.umich.edu/~justincj/slides/eecs498/FA2020/598_FA2020_lecture13.pdf

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Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance.

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Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance.

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