基于共享梯度滤波器的旋转不变限制Boltzmann机
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient Filters
摘要：
寻找合适的特征一直是计算机视觉中的一个基本问题。我们专注于受限玻尔兹曼机（RBM），尽管它们的多功能性，不能适应可能发生在场景中的转换。因此，已经提出了几种考虑一组变换的方法，这些变换用于增强训练集或变换实际学习的滤波器。在本文中，我们提出了显式旋转不变的限制玻尔兹曼机，它利用先验信息来自图像的主导方向。我们的模型扩展了标准的RBM，通过添加适当数量的权重矩阵，与每个主导梯度相关联。我们表明，我们的方法是能够学习旋转不变的功能，比较它与MNIST基准数据集上的RBM的经典配方。总的来说，需要较少的隐藏单元，我们的方法学习紧凑的功能，这是强大的旋转。
英文摘要：
Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As a result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned filters. In this paper, we propose the Explicit Rotation-Invariant Restricted Boltzmann Machine, which exploits prior information coming from the dominant orientation of images. Our model extends the standard RBM, by adding a suitable number of weight matrices, associated with each dominant gradient. We show that our approach is able to learn rotation-invariant features, comparing it with the classic formulation of RBM on the MNIST benchmark dataset. Overall, requiring less hidden units, our method learns compact features, which are robust to rotations.
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