论文标题
资源有效的不变网络：通过展开优化的指数增长
Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization
论文作者
论文摘要
实现滋扰转变的不变性是建立强大而可靠的愿景系统的基本挑战。现有的方法将不变性量表呈指数级的变化范围，使它们无法应付视觉数据中的自然变异性，例如姿势和透视图的变化。我们确定了这些方法的共同局限性 - 它们依靠采样来遍历转换的高维空间 - 并提出了一种基于优化的新计算原始构建不变网络，在许多情况下，它为高维探索提供了一种更有效的方法。我们提供了我们提出方法的效率增长和健全性的经验和理论证实，并证明了其在与独立的优化结合使用时，为简单的层次对象检测任务构建有效的不变网络。我们的网络和实验代码可在https://github.com/sdbuch/refine上获得。
Achieving invariance to nuisance transformations is a fundamental challenge in the construction of robust and reliable vision systems. Existing approaches to invariance scale exponentially with the dimension of the family of transformations, making them unable to cope with natural variabilities in visual data such as changes in pose and perspective. We identify a common limitation of these approaches--they rely on sampling to traverse the high-dimensional space of transformations--and propose a new computational primitive for building invariant networks based instead on optimization, which in many scenarios provides a provably more efficient method for high-dimensional exploration than sampling. We provide empirical and theoretical corroboration of the efficiency gains and soundness of our proposed method, and demonstrate its utility in constructing an efficient invariant network for a simple hierarchical object detection task when combined with unrolled optimization. Code for our networks and experiments is available at https://github.com/sdbuch/refine.
