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Open AccessProceedings ArticleDOI

Going deeper with convolutions

Christian Szegedy, +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.

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Citations
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Proceedings ArticleDOI

Deep Supervised Hashing for Fast Image Retrieval

Haomiao Liu, +3 more
TL;DR: A novel Deep Supervised Hashing method to learn compact similarity-preserving binary code for the huge body of image data and extensive experiments show the promising performance of the method compared with the state-of-the-arts.
Journal ArticleDOI

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Andrea Cossarizza, +462 more
- 01 Oct 2019 - 
TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
Posted Content

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Justin Johnson, +2 more
- 24 Nov 2015 - 
TL;DR: A Fully Convolutional Localization Network (FCLN) architecture is proposed that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with asingle round of optimization.
Proceedings ArticleDOI

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

Yang He, +4 more
TL;DR: He et al. as discussed by the authors proposed a filter pruning via geometric median (FPGM) method to compress CNN models by pruning filters with redundancy, rather than those with relatively less importance.
Posted Content

Action-Conditional Video Prediction using Deep Networks in Atari Games

Junhyuk Oh, +4 more
- 31 Jul 2015 - 
TL;DR: In this article, the authors proposed and evaluated two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, +2 more
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

Jia Deng, +5 more
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, +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

Robert Tibshirani
- 01 Jan 1996 - 
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, +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.
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ImageNet Classification with Deep Convolutional Neural Networks

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Very Deep Convolutional Networks for Large-Scale Image Recognition

Karen Simonyan, +1 more

ImageNet: A large-scale hierarchical image database

Jia Deng, +5 more

Gradient-based learning applied to document recognition

Yann LeCun, +6 more