Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet,
ResNet, GoogleNet, include tens to hundreds of millions of parameters, which
impose considerable computation and memory overhead. This limits their
practical use for training, optimization and memory efficiency. On the
contrary, light-weight architectures, being proposed to address this issue,
mainly suffer from low accuracy. These inefficiencies mostly stem from
following an ad hoc procedure. We propose a simple architecture, called
SimpleNet, based on a set of designing principles, with which we empirically
show, a well-crafted yet simple and reasonably deep architecture can perform on
par with deeper and more complex architectures. SimpleNet provides a good
tradeoff between the computation/memory efficiency and the accuracy. Our simple
13-layer architecture outperforms most of the deeper and complex architectures
to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks
while having 2 to 25 times fewer number of parameters and operations. This
makes it very handy for embedded systems or systems with computational and
memory limitations. We achieved state-of-the-art result on CIFAR10
outperforming several heavier architectures, near state of the art on MNIST and
competitive results on CIFAR100 and SVHN. We also outperformed the much larger
and deeper architectures such as VGGNet and popular variants of ResNets among
others on the ImageNet dataset. Models are made available at:
this https URL

提出了一种简单的卷积神经网络架构 (SimpleNet)，在保持计算和内存效率的同时，具有与更复杂的 CNN 相媲美的性能，在多个基准测试中表现出色，包括 CIFAR-10，MNIST，CIFAR-100 和 SVHN，甚至在 ImageNet 数据集中也优于许多更大更深的 CNN 模型。