Semantic Scholar Open Access 2014 46909 sitasi

Going deeper with convolutions

Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed +4 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (9)

Christian Szegedy

Wei Liu

Yangqing Jia

P. Sermanet

Scott E. Reed

Dragomir Anguelov

D. Erhan

Vincent Vanhoucke

A. Rabinovich

Format Sitasi

APA MLA BibTeX

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D. et al. (2014). Going deeper with convolutions. https://doi.org/10.1109/CVPR.2015.7298594

Akses Cepat

Lihat di Sumber doi.org/10.1109/CVPR.2015.7298594

Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Total Sitasi: 46909×
Sumber Database: Semantic Scholar
DOI: 10.1109/CVPR.2015.7298594
Akses: Open Access ✓