Reconstructing Japanese handwritten images using auto-encoder with residual block in parallel computing
| Autor: | Pamela Kareen, M. Octaviano Pratama |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Dimensionality reduction Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Parallel computing Residual Autoencoder Parallel processing (DSP implementation) Unsupervised learning Artificial intelligence business Block (data storage) |
| Zdroj: | 2017 International Conference on Electrical Engineering and Informatics (ICELTICs). |
| DOI: | 10.1109/iceltics.2017.8253262 |
| Popis: | Unsupervised learning is essential for reconstructing a model without target label such as Reconstructing handwritten character that usually used as benchmarking in deep learning tasks. Auto-encoder is one of unsupervised algorithm with purpose of dimensionality reduction and data reconstruction. Auto-encoder with deep architecture layer will produce noise higher than shallow architecture. In this research, existing Auto-Encoder algorithm augmented with Residual Block is performed in parallel computing to reconstruct Japanese handwritten images. Residual block can strengthen deep layer connection. The result is compared with classical AutoEncoder. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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