Deep Autoencoder Model Construction Based on Pytorch

• URL: http://arxiv.org/abs/2208.08231v1
• Date: Wed, 17 Aug 2022 11:19:05 GMT
• Title: Deep Autoencoder Model Construction Based on Pytorch
• Authors: Junan Pan, Zhihao Zhao
• Abstract summary: This paper introduces the idea of Pytorch into the auto-encoder, and randomly clears the input weights connected to the hidden layer neurons with a certain probability. The new algorithm effectively solves the problem of possible overfitting of the model and improves the accuracy of image classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a deep autoencoder model based on Pytorch. This algorithm introduces the idea of Pytorch into the auto-encoder, and randomly clears the input weights connected to the hidden layer neurons with a certain probability, so as to achieve the effect of sparse network, which is similar to the starting point of the sparse auto-encoder. The new algorithm effectively solves the problem of possible overfitting of the model and improves the accuracy of image classification. Finally, the experiment is carried out, and the experimental results are compared with ELM, RELM, AE, SAE, DAE.

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