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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注意力机制因其优秀的效果与即插即用的便利性,在深度学习任务中得到了越来越广泛的应用。主要着眼于卷积神经网络,对卷积网络注意力机制发展过程中的各种主流方法进行介绍,并对其核心思想与实现过程进行提取与总结,同时对每种注意力机制方法进行实现,针对同型号辐射源设备实测数据进行对比实验与结果分析,并依据主流方法的思想与实验的结果总结并阐述了卷积网络中的注意力机制的研究现状与未来其发展方向。

http://cea.ceaj.org/CN/abstract/abstract39969.shtml

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Understanding when and why interpolating methods generalize well has recently been a topic of interest in statistical learning theory. However, systematically connecting interpolating methods to achievable notions of optimality has only received partial attention. In this paper, we investigate the question of what is the optimal way to interpolate in linear regression using functions that are linear in the response variable (as the case for the Bayes optimal estimator in ridge regression) and depend on the data, the population covariance of the data, the signal-to-noise ratio and the covariance of the prior for the signal, but do not depend on the value of the signal itself nor the noise vector in the training data. We provide a closed-form expression for the interpolator that achieves this notion of optimality and show that it can be derived as the limit of preconditioned gradient descent with a specific initialization. We identify a regime where the minimum-norm interpolator provably generalizes arbitrarily worse than the optimal response-linear achievable interpolator that we introduce, and validate with numerical experiments that the notion of optimality we consider can be achieved by interpolating methods that only use the training data as input in the case of an isotropic prior. Finally, we extend the notion of optimal response-linear interpolation to random features regression under a linear data-generating model that has been previously studied in the literature.

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