基于特征对抗对的视觉特征归因网络研究

Visual Feature Attribution Based on Adversarial Feature Pairs

可视化图像关键特征区域是计算机视觉一个重要而需要深入研究的问题.图像关键特征区域可视化的技术已经在弱监督定位和理解数据隐藏特征的领域中得到广泛应用.近年来,基于神经网络分类器的特征定位显示已成为最新的技术,并且通常用于医学和自然图像数据集上.但存在特征定位显示不精确的缺陷.针对传统神经网络分类器在可视化图像关键特征区域标注方法上的局限性,提出了一种基于生成对抗对特征的关键特征区域可视化方法(即视觉特征归因方法).该方法通过构造关键特征区域对抗对,采用生成和鉴别对抗网络生成关键特征区域,可有效过滤冗余信息并实现精准定位,有效解决了疾病特征可视化问题.在该方法中,为了解决传统生成对抗网络难以达到负载均衡的缺陷,采用了Wasserstein距离解决协调其训练平衡的问题,同时使用梯度惩罚加速收敛过程.在人工合成数据集、肺部数据集和心脏数据集上的实验结果表明,提出的方法在视觉显示的定性和定量的问题中,均产生了理想的真实效果图,非常接近观察到的效果.

Visualizing the key feature of images is an important issue and requires in-depth study for computer vision.Its application ranges from weak supervision in the object localization task to understanding in the hidden features of the data.In medical and natural images data sets,the convolutional neural network-based model has become the latest technology for visualizing the regions of input,which are important for predictions from these models or visual explanations.However,their feature location is not accurate.In view of the limitations of the traditional neural network classifier in the region of the visual image key characteristics,we propose an effective adversarial feature pairs based method for visual feature attribution.In the proposed method,We firstly construct adversarial pair of key feature areas as the input of generative adversarial network(GAN).This makes the generator produce high corresponding key features,and can effectively filter redundant information and achieve accurate position.However,traditional GAN is difficult to produce images that are similar to real images.Therefore,Wasserstein distance and gradient penalty are employed to solve the problem and accelerate the convergence process.Experimental results on synthetic datasets,lung datasets and heart datasets show that our proposed method produces convincing real-world effects in both qualitative and quantitative visual displays.