Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Deep Radial Basis-Function Networks for Open-Set Classification |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | A problem with modern deep learning recognition systems is that they often respond to stimuli of an unknown class overly confident, but wrong. Open-set recognition highlights this behavior and provides evaluation methods to estimate the generalization capability of models beyond the classic train/test set split. In this thesis, we incorporate a Radial Basis Function (RBF) layer into deep convolutional networks to model the deep feature distribution. We evaluate such networks on standard open-set evaluation protocols and compare their performance with standard Softmax classification models. Additionally, we utilize negative training samples and compare with the Entropic Open-Set Loss Softmax extension. We show that standard deep RBF network with Gaussian activation functions does not outperform Softmax based methods in open-set recognition. We extend the RBF network in two ways, which both show increased open-set recognition performance over the baseline RBF network. Based on these results we conjecture that solely using an RBF layer for the classification sub-system of a deep neural network might not be sufficient to solve the open-set recognition problem. |
| PDF File |
Download
|
| Export |
BibTeX
|