Deep Convolutional Neural Network Compression via Coupled Tensor Decomposition

2020 ◽  
pp. 1-1
Author(s):  
Weize Sun ◽  
Shaowu Chen ◽  
Lei Huang ◽  
Hing Cheung So ◽  
Min Xie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tensor Decomposition ◽  
Deep Convolutional Neural Network ◽  
Network Compression

Learning a deep convolutional neural network via tensor decomposition

2021 ◽  
Author(s):  
Samet Oymak ◽  
Mahdi Soltanolkotabi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sample Size ◽  
Gaussian Distribution ◽  
Tensor Decomposition ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Low Rank ◽  
Rank Tensor

Abstract In this paper, we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed deep tensor decomposition (DeepTD) is based on a low-rank tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is sample efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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