论文标题
多型卷积网络
Multipod Convolutional Network
论文作者
论文摘要
在本文中，我们引入了一个卷积网络，我们称之为多磁网，该网络由两个或多个卷积网络组成，它们并行处理输入图像以实现相同的目标。并行卷积网络的输出特征图融合在网络的完全连接层。我们通过实验观察到，三个平行的POD网络（Tripodnet）在常用对象识别数据集中产生最佳结果。基线POD网络可以具有任何类型。在本文中，我们将重新NET用作基线网络，其输入是增强的图像补丁。 Tripodnet的参数数量约为单个重新NET的三倍。我们使用标准反向传播类型算法训练三脚架。在每个单独的重新系统中，在训练过程中以不同的随机数初始化参数。 Tripodnet在CIFAR-10和Imagenet数据集上实现了最新性能。例如，在CIFAR-10数据集的同一训练过程中，它将单个重新系统的准确性从91.66％提高到92.47％。
In this paper, we introduce a convolutional network which we call MultiPodNet consisting of a combination of two or more convolutional networks which process the input image in parallel to achieve the same goal. Output feature maps of parallel convolutional networks are fused at the fully connected layer of the network. We experimentally observed that three parallel pod networks (TripodNet) produce the best results in commonly used object recognition datasets. Baseline pod networks can be of any type. In this paper, we use ResNets as baseline networks and their inputs are augmented image patches. The number of parameters of the TripodNet is about three times that of a single ResNet. We train the TripodNet using the standard backpropagation type algorithms. In each individual ResNet, parameters are initialized with different random numbers during training. The TripodNet achieved state-of-the-art performance on CIFAR-10 and ImageNet datasets. For example, it improved the accuracy of a single ResNet from 91.66% to 92.47% under the same training process on the CIFAR-10 dataset.
