**作者：**Christian Szegedy，Vincent Vanhoucke，Sergey Ioffe，Jonathon Shlens，Zbigniew Wojna
发表期刊：Arxiv
**发表年份：**2015
论文全称：Rethinking the Inception Architecture for Computer Vision
论文地址：https://arxiv.org/abs/1512.00567
代码：https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py
**地位：**本论文在GoogLeNet和BN-Inception的基础上，对Inception模块的结构、性能、参数量和计算效率进行了重新思考和重新设计。提出了Inception V2和Inception V3模型，取得了3.5%左右的Top-5错误率。
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational effificiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as effificiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classifification challenge validation set demonstrate substantial gains over the state of the art: 21*.*2% *top-1 and 5.*6% *top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.*5% *top-5 error and 17.*3% *top-*1 error.