arXiv (Cornell University)· 2014· Preprint
Very Deep Convolutional Networks for Large-Scale Image Recognition
- 75,542citations
- 2014year
Short summary
Increasing convolutional network depth to 16-19 weight layers, using small 3x3 filters, significantly improves accuracy in large-scale image recognition over prior configurations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deeper convolutional networks (16-19 layers) with 3x3 filters outperform previous architectures.
- Achieved 1st and 2nd place in ImageNet Challenge 2014 (classification and localization).
- Representations generalize well to other datasets, setting new state-of-the-art.
- Two best-performing ConvNet models are publicly released.
AI-generated from the title and abstract; the full text is not read.
Abstract
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 (3x3) 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 facilitate further research on the use of deep visual representations in computer vision.
The authors' abstract, as published at the source. arXiv (Cornell University), 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science