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. 2019 Dec 2;9:18113. doi: 10.1038/s41598-019-54653-6

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Illustrating the VAE for visual fields. (A) The top frame demonstrates the structure of the VAE, which is a dual-mapping from the original visual field to the latent features and then back to a reconstructed visual field. (B) The bottom frame provides details on the network architecture. There are four types of layers: convolutional (C), resizing (R), fully connected (F), and de-convolutional (D). The sizes at the bottom of each image reflect the transformed dimensions after each layer. For each layer, the kernel is 3 × 3 with a stride of 2. The activation function for all layers is the rectified non-linear unit, except for F1 and D3, which have the identity and sigmoid activations, respectively. The latent dimension, L, is a user specified parameter.