1. VGG网络
《Very Deep Convolutional Networks For Large Scale Image Recognition》
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3 × 3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014
submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We
have made our two best-performing ConvNet models publicly available to facilitaty further research on the use of deep visual representations in computer vision.
VGG网络分为VGG16和VGG19，16是指卷积层 + 全连接层， 池化层不包括进去，因为池化层没有参数需要进行训练。2.1Architecture提到在VGG中LRN（Local Response Normalization）局部响应归一化并没有起到提升性能的作用，反而增加了内存的消耗和计算时间。LRN在现在的网络中可能已经越来越不被使用了。
All hidden layers are equipped with the rectification (ReLU (Krizhevsky et al., 2012)) non-linearity.
We note that none of our networks (except for one) contain Local Response Normalisation
(LRN) normalisation (Krizhevsky et al., 2012): as will be shown in Sect. 4, such normalisation
does not improve the performance on the ILSVRC dataset, but leads to increased memory consumption
and computation time. Where applicable, the parameters for the LRN layer are those
of (Krizhevsky et al., 2012).