From 3501a2b212b5e5103e5a8be87e092c7b9d25f6f9 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Tue, 19 Feb 2019 15:43:52 +0100 Subject: [PATCH] Added VGG paper Signed-off-by: Jim Martens --- ma.bib | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/ma.bib b/ma.bib index 56c242c..673fe58 100644 --- a/ma.bib +++ b/ma.bib @@ -518,4 +518,19 @@ to construct explicit models for non-normal classes. Application includes infere timestamp = {2019.01.05}, } +@Article{Simonyan2014, + author = {Karen Simonyan and Andrew Zisserman}, + title = {Very Deep Convolutional Networks for Large-Scale Image Recognition}, + journal = {arXiv preprint}, + date = {2014-09-04}, + eprint = {http://arxiv.org/abs/1409.1556v6}, + eprintclass = {cs.CV}, + eprinttype = {arXiv}, + 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.}, + file = {:/home/jim/Documents/Studium/MA/Literatur/35_VGG.pdf:PDF}, + keywords = {cs.CV}, + owner = {jim}, + timestamp = {2019.02.19}, +} + @Comment{jabref-meta: databaseType:biblatex;}