DenseNets have been shown to be a competitive model among recent convolutional network architectures. These networks utilize Dense Blocks, which are groups of densely connected layers where the output of a hidden layer is fed in as the input of every other layer following it. In this paper, we aim to improve certain aspects of DenseNet, especially when it comes to practicality. We introduce ParaNet, a new architecture that constructs three pipelines which allow for early inference. We additionally introduce a cascading mechanism such that different pipelines are able to share parameters, as well as logit matching between the outputs of the pipelines. We separately evaluate each of the newly introduced mechanisms of ParaNet, then evaluate our proposed architecture on CIFAR-100.
翻译:DenseNet是最近革命网络结构中的一个竞争模式。这些网络利用了Dense Blocks, 它们是密连层群,其中隐藏层的输出被输入作为其后每个层的投入。在本文中,我们的目标是改进DenseNet的某些方面,特别是在实用性方面。我们引入了ParaNet,这是一个建造三条管道的新结构,可以进行早期推断。我们还引入了一种连锁机制,使不同的管道能够分享参数,以及管道产出之间的登录匹配。我们分别评估了ParaNet新引入的每个机制,然后评估了我们在CIFAR-100上的拟议结构。