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Rethinking the Inception Architecture for Computer Vision
- Author: Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
- Publish Year: 2015
This paper explores ways to scale up convolutional networks for computer vision tasks with high efficiency and accuracy. It reports substantial gains over the state of the art on the ILSVRC 2012 classification challenge validation set.Rethinking the Inception Architecture for Computer Vision
Dec 12, 2016 · A paper published in 2016 CVPR that explores ways to scale up convolutional networks efficiently and achieve state of the art results on ILSVRC 2012 classification …
This paper explores ways to scale up convolutional networks efficiently by using factorized convolutions and aggressive regularization. It presents a new Inception-style network that …
Rethinking the Inception Architecture for Computer …
Dec 2, 2015 · 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 …
Rethinking the Inception Architecture for Computer Vision
Learn how Google researchers scaled up convolutional networks for computer vision tasks with factorized convolutions and regularization. See the results and methods for ILSVRC 2012 …
CVPR 2016 Open Access Repository - cv-foundation.org
Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, Zbigniew Wojna ; Proceedings of the IEEE Conference on Computer …
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Rethinking the Inception Architecture for Computer Vision
A paper that explores ways to scale up convolutional networks for computer vision tasks with low parameter count and high efficiency. It presents a new network design with factorized …
Rethinking the Inception Architecture for Computer Vision - ar5iv
A paper that explores ways to scale up convolutional networks efficiently by using factorized convolutions and aggressive regularization. It presents a new Inception-style network that …
Rethinking the Inception Architecture for Computer Vision
TL;DR: This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable …
Rethinking the Inception Architecture for Computer Vision
Dec 2, 2015 · A paper that explores ways to scale up convolutional networks for computer vision tasks with high efficiency and accuracy. It reports substantial gains over the state of the art on …
of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in vari-ous benchmarks. …
Rethinking the Inception Architecture for Computer Vision
Starting in 2014, the quality of network architec- methods could be applied to optimize the Inception archi- tures significantly improved by utilizing deeper and wider tecture as well, …
Rethinking the Inception Architecture for Computer Vision
Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive …
Rethinking the Inception Architecture for Computer Vision
This review offers an extensive analysis of this rapidly evolving field by examining four key areas: the development of static or dynamic light-weighted backbone models for the efficient …
Rethinking the Inception Architecture for Computer Vision
Dec 2, 2015 · The authors propose and discuss several design principles that help scale up neural networks efficiently, and that they use to guide the design of their networks, Inception V2 and …
volutional architecture can be utilized for improving perfor- mance for most other computer vision tasks that are increas- ingly reliant on high quality, learned visual features.
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become …
Rethinking the Inception Architecture for Computer Vision
Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive …
Rethinking the Inception Architecture for Computer Vision
Dec 2, 2015 · 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 …
Rethinking the Inception Architecture for Computer Vision
Dec 2, 2015 · Here we explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive …
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