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Scientific Research Publishing2mon
Du, B., Xiong, W., Wu, J., Zhang, L., Zhang, L. and Tao, D. (2017) Stacked Convolutional Denoising Auto-Encoders for Feature Representation. IEEE Transactions on Cybernetics ...
The experimental results show that this method can effectively integrate the channel attention module and the fully convolutional autoencoder. Although it is an unsupervised feature learning model, it ...
C&EN3y
Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder
La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia ...
C&EN3y
Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis
A deep convolutional neural network with deconvolution and a deep autoencoder (DDD) is proposed ... The CNNs used in image analysis comprise a feature extraction part that stacks convolutional and ...
Frontiers3y
Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder
After feature extraction, the input format of each sample data in convolutional autoencoder is an array of 9 × 9 × 4. The value at each position in the array represents the differential entropy ...
Frontiers3y
Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets
Feature extraction: the model trained using the Kyoto dataset ... 3D, three-dimensional; CAE, convolutional autoencoder; COBRE, Center for Biomedical Research Excellence; CPZE, dose of antipsychotic ...
unite4y
What is an Autoencoder?
If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder ... data denoising, feature extraction, image generation, sequence to sequence prediction, ...