【文件属性】:
文件名称:【6】Going deeper with convolutions.pdf
文件大小:1.24MB
文件格式:PDF
更新时间:2022-09-15 04:36:39
ai 机器学习 深度学习 学术论文
We propose a deep convolutional neural network architecture
codenamed Inception that achieves the new
state of the art for classification and detection in the ImageNet
Large-Scale Visual Recognition Challenge 2014
(ILSVRC14). The main hallmark of this architecture is the
improved utilization of the computing resources inside the
network. By a carefully crafted design, we increased the
depth and width of the network while keeping the computational
budget constant. To optimize quality, the architectural
decisions were based on the Hebbian principle and
the intuition of multi-scale processing. One particular incarnation
used in our submission for ILSVRC14 is called
GoogLeNet, a 22 layers deep network, the quality of which
is assessed in the context of classification and detection.