论文标题
学会在具有可区分二进制嵌入网络的蕨类植物之间映射
Learning to map between ferns with differentiable binary embedding networks
论文作者
论文摘要
当前的深度学习方法基于具有参数密集型重量矩阵的卷积的重复应用。在这项工作中，我们提出了一个新颖的概念，可以在端到端网络中应用可区分的随机蕨类植物。然后，它可以用作深网架构中的无繁殖卷积层替代方案。我们对TUPAC'16挑战的二进制分类任务的实验表明，与最先进的二进制XNOR NET相比，结果改善了结果，并且性能仅比其2倍的参数密集浮点CNN CNN对应物稍差。
Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices. In this work, we present a novel concept that enables the application of differentiable random ferns in end-to-end networks. It can then be used as multiplication-free convolutional layer alternative in deep network architectures. Our experiments on the binary classification task of the TUPAC'16 challenge demonstrate improved results over the state-of-the-art binary XNOR net and only slightly worse performance than its 2x more parameter intensive floating point CNN counterpart.
