基于特征可视化分析深度神经网络的内部表征

基于可视化的方式理解深度神经网络能直观地揭示其工作机理,即提供了黑盒模型做出决策的解释,在医疗诊断、自动驾驶等领域尤其重要。大部分现有工作均基于激活值最大化框架,即选定待观测神经元,通过优化输入值(如隐藏层特征图谱、原始图片),定性地将待观测神经元产生最大激活值时输入值的改变作为一种解释。然而,这种方法缺乏对深度神经网络深入的定量分析。文中提出了结构可视化和基于规则可视化两种可视化的元方法。结构可视化从浅至深依层可视化,发现浅层神经元具有一般性的全局特征,而深层神经元更针对细节特征。基于规则可视化包括交集与差集规则,可以帮助发现共享神经元与抑制神经元的存在,它们分别学习了不同类别的共有特征与抑制不相关的特征。实验针对代表性卷积网络VGG和残差网络ResNet在ImageNet和微软COCO数据集上进行了分析。通过量化分析发现,ResNet和VGG均有很高的稀疏性,通过屏蔽一些低激活值的"噪音"神经元,发现其对深度神经网络分类准确率均没有影响,甚至有一定程度的提高作用。文中通过可视化和量化分析深度神经网络的隐藏层特征,揭示其内部特征表达,从而为高性能深度神经网络的设计提供指导和借鉴。

Analyzing Latent Representation of Deep Neural Networks Based on Feature Visualization

The working mechanism of deep neural networks can be intuitively uncovered by visualization technique.Visualizing deep neural networks can provide the interpretability on the decision made by the black box model,which is critically important in many fields,such as medical diagnosis and autopilot.Current existing works are mostly based on the activation maximization technique,which optimizes the input,the hidden feature map or the original image,in condition to the neuron that we want to observe.Qualitatively,the change in the input value can be taken as explanation when the neuron has reached nearly the maximum activation value.However,such method lacks the quantitative analysis of deep neural networks.To fill this gap,this paper proposes two meta methods,namely,structure visualization and rule-based visualization.Structure visualization works by visualizing from the shallow layers to the deep layers,and find that neurons in shallow layers learn global characteristics while neurons in deep layers learn more specific features.The rule-based visualization includes intersection and difference selection rule,and it is helpful to find the existence of shared neurons and inhibition neurons that learns the common features of different categories and inhibits unrelated features respectively.Experiments on two representative deep networks,namely the convolutional network VGG and the residual network ResNet,by using ImageNet and COCO datasets.Quantitative analysis shows that ResNet and VGG are highly sparse in representation.Thus,by removing some low activation-value“noisy”neurons,the networks can keep or even improve the classification accuracy.This paper discovers the Latent representation of deep neural networks by visualizing and quantitatively analyzing hidden features,thus providing guidance and reference for the design of high-performance deep neural networks.