We propose DAEMA (Denoising Autoencoder with Mask Attention), an algorithm based on a denoising autoencoder architecture with an attention mechanism. While most imputation algorithms use incomplete inputs as they would use complete data - up to basic preprocessing (e.g. mean imputation) - DAEMA...
This repository contains the code used for the paper DAEMA: Denoising Autoencoder with Mask Attention. The documentation of the code, generated by sphinx, is available here. Please cite as @article{tihon2021daema, title={DAEMA: Denoising Autoencoder with Mask Attention}, author={Tihon, Simon...
In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE). We decompose this decoding mechanism for masked patch reconstruction in MAE into self-attention and cross-attention. Our investigations suggest that self-attention between mask patches is not...
However, it remains a significant challenge to accurately identify spatial domains with similar gene expression, which requires efficient combination of gene expression data, histology image information, and spatial location. To address this challenge, a novel dual denoising autoencoder with attention ...
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To analyze the latent dimension's effect on the quality of the synthesized images, we trained the VQ-GAN autoencoder with two different compression factors. The compression factor describes the factor by which the original dimension of the image is reduced in each corresponding dimension of the la...
To analyze the latent dimension's effect on the quality of the synthesized images, we trained the VQ-GAN autoencoder with two different compression factors. The compression factor describes the factor by which the original dimension of the image is reduced in each corresponding dimension of the la...
We examine the dense fea- ture maps before each (average or attention)-pooling oper- ation in the CLIP ResNet image encoder. (Refer Alg. 1 in the Supplementary Material for details). This yields a to- tal of five multi-scale features, denoted as F1 ∈ H R2 × W 2 ×C and Fi ...
An advanced fault diagnosis approach for wind turbine planetary gearbox based on optimized multi-layer attention denoising autoencoders 来自 IOP 喜欢 0 阅读量: 6 作者:L Lu,S Wang,X Yu,T Wang,B Li,Y He 摘要: Fault diagnosis of wind turbine planetary gearboxes is essential for maintaining ...