Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Going deeper with convolutions
46.236
Zitationen
9
Autoren
2015
Jahr
Abstract
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.
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 215.868 Zit.
U-Net: Convolutional Networks for Biomedical Image Segmentation
2015 · 85.833 Zit.
ImageNet classification with deep convolutional neural networks
2017 · 75.547 Zit.
Very Deep Convolutional Networks for Large-Scale Image Recognition
2014 · 75.404 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 52.596 Zit.