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DNN (Deep Neural Network) based training method of image coder and decoder

A deep neural network and training method technology, applied in biological neural network models, neural architectures, instruments, etc., to avoid unstable training

Active Publication Date: 2018-09-25
ZHEJIANG UNIV OF TECH
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there are often more than 2 error functions and more than 3 functional modules in complex image codecs

Method used

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  • DNN (Deep Neural Network) based training method of image coder and decoder
  • DNN (Deep Neural Network) based training method of image coder and decoder
  • DNN (Deep Neural Network) based training method of image coder and decoder

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Embodiment Construction

[0020] The present invention will be further described below in conjunction with the accompanying drawings.

[0021] refer to Figure 1 ~ Figure 3 , a kind of training method of the image codec based on deep neural network, described training method comprises the following steps:

[0022] The first step, spatial decoupling: used to decouple the codec and the generation model, and decouple the hidden variable encoding and reconstruction module;

[0023] The second step, time divide and conquer: optimize different loss functions and use different learning rates at different stages of training the codec to improve the speed and stability of training.

[0024] Further, the spatial decoupling aggregates mutually interfering loss functions in the codec into a module, and optimizes the loss function by module during training.

[0025] Furthermore, the modules aggregated according to the spatial decoupling method are decoupled, that is, when a certain module is optimized, it will no...

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Abstract

The invention relates to a DNN based training method of an image coder and decoder. The training method comprises the following steps of 1) spatial decoupling in which the coder and decoder is decoupled from a generation model, and hidden variable coding is decoupled from a reconstruction module; and 2) treatment according to time, in which different loss functions are optimized in different training phases of the coder-decoder, and different learning rates are used to improve the training speed and stability. The method can be used to avoid mutual interference among error functions effectively.

Description

technical field [0001] The invention belongs to the field of image codecs, in particular to a training method for image codecs based on a deep neural network. Background technique [0002] For image codecs based on deep neural networks, it is usually necessary to optimize multiple loss functions at the same time during network training, such as reconstruction error functions and image generation against error functions. At the same time, in practical applications, other loss functions will be additionally optimized according to specific needs. These different loss functions have a significant coupling relationship, which will cause serious conflict problems in network training. If the proportion of different error functions is not appropriate, it will lead to unstable training, affecting the reconstruction accuracy of the decoder for the image and the simulation of the generated image, that is, affecting the similarity between the coded image and the training image set . ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 周乾伟陶鹏陈禹行詹琦梁胡海根李小薪陈胜勇
Owner ZHEJIANG UNIV OF TECH