The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision. Both of these methodologies have separately achieved a great deal of success in many computer vision tasks. However, there has been little work attempting to leverage the power of these to methodologies jointly. To this end, this paper presents the Deep Mean Maps (DMMs) framework, a novel family of methods to non-parametrically represent distributions of features in convolutional neural network models. DMMs are able to both classify images using the distribution of top-level features, and to tune the top-level features for performing this task. We show how to implement DMMs using a special mean map layer composed of typical CNN operations, making both forward and backward propagation simple. We illustrate the efficacy of DMMs at analyzing distributional patterns in image data in a synthetic data experiment. We also show that we extending existing deep architectures with DMMs improves the performance of existing CNNs on several challenging real-world datasets.

翻译：使用深层结构的分布图和高层次特征已成为现代计算机愿景中常见的。这两种方法分别在许多计算机愿景任务中取得了巨大成功。然而,几乎没有人试图利用这些功能的力量来共同使用方法。为此,本文件介绍了深平均值图框架,这是将各种新颖的方法组合,非对称地代表进化神经网络模型特征的分布。DMM能够利用顶层特征的分布对图像进行分类,并调整执行这项任务的顶层特征。我们展示了如何使用由典型的CNN操作组成的特殊平均地图层执行DMM,使前向和后向传播变得简单。我们展示了DMMM在合成数据实验中分析图像数据分布模式的功效。我们还表明,我们通过DMMS扩展现有的深层结构,提高了现有CNN在几个具有挑战性的真实世界数据集上的性能。