Samenvatting
Neuro-Evolution is a field of study that has recently gained significantly increased traction in the deep learning community. It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks. Recent Neuro-Evolution approaches have shown promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created that efficiently compresses the input data in the first step, and a convolutional neural network is created to classify the compressed data in the second step. The creation of networks in both steps is guided by by an evolutionary process, where new networks are constantly being generated by mutating members of a collection of existing networks. Additionally, a method is introduced that considers the trade-off between compression and information loss of different convolutional autoencoders. This is used to select the optimal convolutional autoencoder from among those evolved to compress the data for the second step.
The complete framework is implemented, tested on the popular CIFAR-10 data set, and the results are discussed. Finally, a number of possible directions for future work with this particular framework in mind are considered, including opportunities to improve its efficiency and its application in particular areas.
A two-step approach is introduced where a convolutional autoencoder is created that efficiently compresses the input data in the first step, and a convolutional neural network is created to classify the compressed data in the second step. The creation of networks in both steps is guided by by an evolutionary process, where new networks are constantly being generated by mutating members of a collection of existing networks. Additionally, a method is introduced that considers the trade-off between compression and information loss of different convolutional autoencoders. This is used to select the optimal convolutional autoencoder from among those evolved to compress the data for the second step.
The complete framework is implemented, tested on the popular CIFAR-10 data set, and the results are discussed. Finally, a number of possible directions for future work with this particular framework in mind are considered, including opportunities to improve its efficiency and its application in particular areas.
| Originele taal-2 | Engels |
|---|---|
| Titel | Machine Learning, Optimization, and Data Science - 4th International Conference, LOD 2018, Revised Selected Papers |
| Redacteuren | Giuseppe Nicosia, Giovanni Giuffrida, Giuseppe Nicosia, Panos Pardalos, Vincenzo Sciacca, Renato Umeton |
| Plaats van productie | Cham |
| Uitgeverij | Springer |
| Pagina's | 293-304 |
| Aantal pagina's | 12 |
| ISBN van elektronische versie | 978-3-030-13709-0 |
| ISBN van geprinte versie | 978-3-030-13708-3 |
| DOI's | |
| Status | Gepubliceerd - feb. 2019 |
| Evenement | 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 - Volterra, Italië Duur: 13 sep. 2018 → 16 sep. 2018 |
Publicatie series
| Naam | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11331 LNCS |
| ISSN van geprinte versie | 0302-9743 |
| ISSN van elektronische versie | 1611-3349 |
Congres
| Congres | 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 |
|---|---|
| Land/Regio | Italië |
| Stad | Volterra |
| Periode | 13/09/18 → 16/09/18 |
Financiering
Acknowledgements. Research leading to these results has received funding from the EU ECSEL Joint Undertaking under grant agreement no. 737459 (project Productive4.0) and from Philips Research.