{"id":"src_m9cl62s2pvmm","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","authorName":"Karen Simonyan, Andrew Zisserman","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.","tags":["computer vision","convolutional neural networks","deep learning","image recognition"],"priceUSDC":"0.0001"}