Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression
Autoencoders are feedforwardneural networks which attempt to reconstruct the input data at the output layer. Since the hidden layer in the autoencoders is smaller than the input layer, the dimensionality of input data is reduced to a smaller dimensional code space at the hidden layer. The reduced co...
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| Format: | Thesis |
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2008
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| Online Access: | http://shdl.mmu.edu.my/1563/ |
| _version_ | 1848789824343900160 |
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| author | Tan, Chun Chet |
| author_facet | Tan, Chun Chet |
| author_sort | Tan, Chun Chet |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Autoencoders are feedforwardneural networks which attempt to reconstruct the input data at the output layer. Since the hidden layer in the autoencoders is smaller than the input layer, the dimensionality of input data is reduced to a smaller dimensional code space at the hidden layer. The reduced codes from the hidden layer are then reconstructed back into the original data at the output layer. Like Principal Component Analysis (PCA), the autoencoders can give mappings in both directions between the data and the codes. |
| first_indexed | 2025-11-14T18:02:51Z |
| format | Thesis |
| id | mmu-1563 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:02:51Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-15632010-09-23T06:46:42Z http://shdl.mmu.edu.my/1563/ Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression Tan, Chun Chet QA76.75-76.765 Computer software Autoencoders are feedforwardneural networks which attempt to reconstruct the input data at the output layer. Since the hidden layer in the autoencoders is smaller than the input layer, the dimensionality of input data is reduced to a smaller dimensional code space at the hidden layer. The reduced codes from the hidden layer are then reconstructed back into the original data at the output layer. Like Principal Component Analysis (PCA), the autoencoders can give mappings in both directions between the data and the codes. 2008-05 Thesis NonPeerReviewed Tan, Chun Chet (2008) Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression. Masters thesis, Multimedia University. http://myto.perpun.net.my/metoalogin/logina.php |
| spellingShingle | QA76.75-76.765 Computer software Tan, Chun Chet Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title | Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title_full | Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title_fullStr | Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title_full_unstemmed | Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title_short | Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression |
| title_sort | autoencoder neural networks: a performance study based on image recognition, reconstruction and compression |
| topic | QA76.75-76.765 Computer software |
| url | http://shdl.mmu.edu.my/1563/ http://shdl.mmu.edu.my/1563/ |