Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- pp 1-9
TLDR
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).Abstract:
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.read more
Citations
More filters
Proceedings ArticleDOI
DRISA: a DRAM-based Reconfigurable In-Situ Accelerator
TL;DR: DRISA, a DRAM-based Reconfigurable In-Situ Accelerator architecture, is proposed to provide both powerful computing capability and large memory capacity/bandwidth to address the memory wall problem in traditional von Neumann architecture.
Posted Content
Practical Block-wise Neural Network Architecture Generation
TL;DR: A block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy and offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs.
Journal ArticleDOI
Fingerprint Liveness Detection Using Convolutional Neural Networks
TL;DR: It is shown that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection, and data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones.
Proceedings ArticleDOI
Gated Context Aggregation Network for Image Dehazing and Deraining
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end gated context aggregation network to directly restore the final haze-free image, which adopted the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-network to fuse the features from different levels.
Journal ArticleDOI
Deep Learning for Automated Skeletal Bone Age Assessment in X-Ray Images
TL;DR: This paper proposes and test several deep learning approaches to assess skeletal bone age automatically and shows an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state‐of‐the‐art performance.
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.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.