| Grant number: | 16/21243-7 |
| Support type: | Scholarships abroad - Research Internship - Master's degree |
| Effective date (Start): | February 01, 2017 |
| Effective date (End): | May 31, 2017 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | João Paulo Papa |
| Grantee: | Gustavo Henrique de Rosa |
| Supervisor abroad: | Gustavo Kunde Rohde |
| Home Institution: | Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil |
| Local de pesquisa : | University of Virginia (UVa), United States |
| Associated to the scholarship: | 15/25739-4 - On the Study of Semantics in Deep Learning Models, BP.MS |
Abstract Deep learning-based approaches have been paramount in the last years, mainly due to their outstanding results in several application domains, that range from face and object recognition to handwritten digits identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanism. However, one main shortcoming of such models concerns their overfitting problem, which prevents the network from predicting unseen data effectively. In this proposal, we address this problem by means of proper selecting regularization parameters by means of meta-heuristic-driven techniques, which provide a simple and elegant solution to a number of optimization problems. The proposed approach will be validated in the context of nuclei detection in general-purpose cell images under the supervision of Prof. Gustavo Rohde, University of Virginia. | |