The Learning Effect of Different Hidden Layers Stacked Autoencoder
2016pp. 148–151
Citations Over TimeTop 16% of 2016 papers
Abstract
Stacked autoencoder is a typical deep neural network. The hidden layers will compress the input data with a better representation than the raw data. Stacked autoencoder has several hidden layers. However, the number of hidden layers is always experiential. In this paper, different hidden layers number autoencoders are discussed. Different depths of stacked autoencoder have different learning capability. The deeper stacked autoencoders have better learning capability which needs more training iterations and time.
Related Papers
- → Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training(2020)23 cited
- → Simultaneous Cell Detection and Classification with an Asymmetric Deep Autoencoder in Bone Marrow Histology Images(2017)8 cited
- → A Novel Harris Hawk Algorithm (HHA) to Optimize Deep Autoencoder (DAE) Method of Deep Learning for Cancer Diagnosis(2023)2 cited
- → Residual Sparse Autoencoders for Unsupervised Feature Learning and Its Application to HEp-2 Cell Staining Pattern Recognition(2019)1 cited
- → A deep learning approach for stock market prediction using deep autoencoder and long short-term memory(2021)1 cited