论文标题
Thriftynets：具有微小参数预算的卷积神经网络
ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
论文作者
论文摘要
典型的深卷积体系结构随着我们在网络中的深度进度时，越来越多的特征地图，而通过下采样操作减少了输入的空间分辨率。这意味着大多数参数都在最终层中，而大部分计算是由第一层中总参数的一小部分执行的。为了最大程度地使用网络的每个参数，我们提出了一种新的卷积神经网络体系结构，称为Thriftynet。在Thriftynet中，仅定义和递归定义一个卷积层，导致最大参数分解。在补体中，归一化，非线性，下降采样和捷径可确保模型的足够表达性。 Thriftynet在很小的参数预算上实现了竞争性能，总计CIFAR-10的准确性超过了91％的准确性，总参数少于40K，而CIFAR-100的竞争精度为74.3％，参数少于600K。
Typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations. This means that most of the parameters lay in the final layers, while a large portion of the computations are performed by a small fraction of the total parameters in the first layers. In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet. In ThriftyNet, only one convolutional layer is defined and used recursively, leading to a maximal parameter factorization. In complement, normalization, non-linearities, downsamplings and shortcut ensure sufficient expressivity of the model. ThriftyNet achieves competitive performance on a tiny parameters budget, exceeding 91% accuracy on CIFAR-10 with less than 40K parameters in total, and 74.3% on CIFAR-100 with less than 600K parameters.
