In this paper, we build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks and disclose the effectiveness of attention modules more straightforwardly to fully exploit their potential. Given an existing neural network equipped with arbitrary attention modules, we introduce a meta critic network to evaluate the quality of attention maps in the main network. Due to the discreteness of our designed reward, the proposed learning method is arranged in a reinforcement learning setting, where the attention actors and recurrent critics are alternately optimized to provide instant critique and revision for the temporary attention representation, hence coined as Deep REinforced Attention Learning (DREAL). It could be applied universally to network architectures with different types of attention modules and promotes their expressive ability by maximizing the relative gain of the final recognition performance arising from each individual attention module, as demonstrated by extensive experiments on both category and instance recognition benchmarks.
翻译:在本文中,我们以任何进化神经网络中受监督的中间关注图生成机制为基础,更直接地披露关注模块的有效性,以充分利用其潜力。鉴于现有的神经网络配备了任意关注模块,我们引入了元批评网络,以评价主网络中关注图的质量。由于我们设计奖励的离散性,拟议的学习方法安排在强化学习环境中,关注行为体和经常批评者轮流优化,为临时关注代表提供即时批评和修改,因此被创建为“深度持续关注学习 ” ( DREAL ) 。它可以普遍适用于具有不同类型关注模块的网络结构,并通过最大限度地提高每个单个关注模块最终承认业绩的相对收益,通过对类别和实例识别基准的广泛实验所显示的每个关注模块最终承认业绩的相对收益,促进其表达能力。