goingDeeperWithConvolutions: Difference between revisions
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= Related work = | = Related work = | ||
In 2013 | In 2013 Lin et al.<ref> | ||
Min Lin, Qiang Chen and Shuicheng Yan. Network in Network | Min Lin, Qiang Chen and Shuicheng Yan. [http://arxiv.org/pdf/1312.4400v3.pdf Network in Network] | ||
</ref> pointed out that the convolution filter in CNN is a generalized linear model (GLM) for the underlying data patch and the level of abstraction is low with GLM. They suggested replacing GLM with a ”micro network” structure which is a general nonlinear function approximator. | </ref> pointed out that the convolution filter in CNN is a generalized linear model (GLM) for the underlying data patch and the level of abstraction is low with GLM. They suggested replacing GLM with a ”micro network” structure which is a general nonlinear function approximator. | ||
[[File:nin.tiff]] | [[File:nin.tiff]] | ||
Revision as of 16:13, 20 October 2015
Introduction
In the last three years, due to the advances of deep learning and more concretely convolutional networks. [an introduction of CNN] , the quality of image recognition has increased dramatically. The error rates for ILSVRC competition dropped significantly year by year.[LSVRC] This paper proposed a new deep convolutional neural network architecture codenamed Inception. With the inception module and carefully crafted design researchers build a 22 layers deep network called Google Lenet, which uses 12X fewer parameters while being significantly more accurate than the winners of ILSVRC 2012.<ref> Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. </ref> [1]
Related work
In 2013 Lin et al.<ref> Min Lin, Qiang Chen and Shuicheng Yan. Network in Network </ref> pointed out that the convolution filter in CNN is a generalized linear model (GLM) for the underlying data patch and the level of abstraction is low with GLM. They suggested replacing GLM with a ”micro network” structure which is a general nonlinear function approximator. File:nin.tiff
References
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