Abstract
Deep learning algorithm has been widely used in many area which is one of the most important representation learning algorithms in machine learning tasks. Deep learning network is stacked by the building blocks such as the restricted Boltzmann machine(RBM) and the auto-encoder, convolutional building block. After stacking the building blocks layers and layers, the improvement of the deep learning network would be notable. In this paper, we proposed a new deep learning building block that inspired by the auto-encoder, which is the compressed auto-encoder with fewer layers and parameters compared with the auto-encoder, and we put forward a bidirectional gradient decent method to update the parameters of this building block. As the experimental results show that improves the performance of the auto-encoder in accuracy of the reconstruction data. It keeps declining the error while the results of rbm or the auto-encoder becomes saturation, and some analysis are given in this paper.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.