Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
Reads0
Chats0
TLDR
In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.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.read more
Citations
More filters
Proceedings ArticleDOI
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
TL;DR: Zhang et al. as discussed by the authors proposed a patch-patch context between image regions and patch-background context, and formulated conditional random fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
Journal ArticleDOI
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
TL;DR: An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge.
Proceedings ArticleDOI
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
TL;DR: PipeLayer is presented, a ReRAM-based PIM accelerator for CNNs that support both training and testing and proposes highly parallel design based on the notion of parallelism granularity and weight replication, which enables the highly pipelined execution of bothTraining and testing, without introducing the potential stalls in previous work.
Proceedings ArticleDOI
Deep Pyramidal Residual Networks
TL;DR: This research gradually increases the feature map dimension at all units to involve as many locations as possible in the network architecture and proposes a novel residual unit capable of further improving the classification accuracy with the new network architecture.
Proceedings ArticleDOI
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
TL;DR: This paper presents an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks, and performs extremely well in the challenging KITTI dataset.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).