论文标题
计算机视觉的总变化优化层
Total Variation Optimization Layers for Computer Vision
论文作者
论文摘要
在深网的一层中的优化已成为深网设计的新方向。但是，将这些层应用于计算机视觉任务时有两个主要挑战：（a）一层中的哪个优化问题有用？ （b）如何确保层中的计算保持有效？为了研究问题（a），在这项工作中，我们提出了最小化（电视）作为计算机视觉层的一层。由于图像处理中总变化的成功，我们假设电视作为一层也为深网提供了有用的电感偏见。我们在五个计算机视觉任务上研究了这一假设：图像分类，弱监督的对象定位，边缘保护平滑，边缘检测和图像denosing，对现有基线的改善。为了实现这些结果，我们必须解决问题（b）：我们开发了一种基于GPU的投影Newton方法，该方法比现有解决方案快37美元\ times $。
Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?; (b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projected-Newton method which is $37\times$ faster than existing solutions.
