Name
Abstract | ||
|---|---|---|
In recent years Convolutional Neural Networks (CNN) have come to dominate many machine learning tasks, specially those related to image analysis, such as object recognition. Herein we explore the possibility of developing image denoising filters by stacking multiple Genetic Programming (GP) syntax trees, in a similar fashion to how CNNs are designed. We test the evolved filters performance in removing additive Gaussian noise. Results show that GP is able to generate a diverse set of feature maps at the 'hidden' layers of the proposed architecture. Although more research is required to validate the suitability of GP for image denoising, our work set the basis for bridging the gap between deep learning and evolutionary computation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-21077-9_5 | PATTERN RECOGNITION, MCPR 2019 |
Keywords | DocType | Volume |
Deep Genetic Programming, Evolutionary machine learning, Genetic Programming, Image filtering, Deep Learning | Conference | 11524 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lino Rodriguez-Coayahuitl | 1 | 0 | 0.34 |
| Alicia Morales-Reyes | 2 | 70 | 11.55 |
| Hugo Jair Escalante | 3 | 939 | 73.89 |