Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
DidEye: An Attempt for Defining and Incorporating Visual Intuition for Convolutional Neural Networks DidEye: Bir Görsel Sezgi Tanimlama Denemesi ve Evrişimsel Sinir Aǧlarina Uygulanmasi
Date
2023-01-01
Author
Koç, Robin
Yarman Vural, Fatoş Tunay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
46
views
0
downloads
Cite This
Is it possible to formally define one of the important capabilities of human mind, the intuition, in a mathematical sense? Can we use this definition to develop more robust CNN (Convolutional Neural Networks) model? Through this method, can we develop an algorithm that can recognize incomplete images? In this study, we attempt to find partial answers to the above questions. First, we examined how the performance of CNN algorithms decreased by reducing the amount of information in the test set images in a controlled manner. In order to reduce this decrease, we made a mathematical definition of intuition that could enrich the convolutional neural networks. We used this definition and the intuition module we created to improve the filter outputs in the convolution layer. We used MNIST number dataset to measure the performance of this new CNN model, enriched with an intuition module, called, DidEye performance. Experimental results show that the suggested DidEye model is much more robust and provides higher performance compared to the classical CNN model in a test set containing incomplete images.
Subject Keywords
CNN
,
information loss
,
intuition
,
machine learning
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173432505&origin=inward
https://hdl.handle.net/11511/105736
DOI
https://doi.org/10.1109/siu59756.2023.10223803
Conference Name
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Collections
Department of Computer Engineering, Conference / Seminar
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
R. Koç and F. T. Yarman Vural, “DidEye: An Attempt for Defining and Incorporating Visual Intuition for Convolutional Neural Networks DidEye: Bir Görsel Sezgi Tanimlama Denemesi ve Evrişimsel Sinir Aǧlarina Uygulanmasi,” presented at the 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, İstanbul, Türkiye, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173432505&origin=inward.