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Impact of fully connected layers on performance of convolutional neural networks for image classification
- Source :
- Neurocomputing. 378:112-119
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/ shallower) is better suitable for which kind of (deeper/ wider) datasets. To address these findings, we have performed experiments with three CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of the image classification problem. The source code of this research is available at https://github.com/shabbeersh/Impact-of-FC-layers.<br />This paper is accepted for publication in Neurocomputing Journal
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
0209 industrial biotechnology
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Cognitive Neuroscience
Computer Science - Computer Vision and Pattern Recognition
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
Convolutional neural network
Machine Learning (cs.LG)
020901 industrial engineering & automation
Artificial Intelligence
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
Selection (linguistics)
Neural and Evolutionary Computing (cs.NE)
Thesaurus (information retrieval)
Contextual image classification
business.industry
Image and Video Processing (eess.IV)
Process (computing)
Computer Science - Neural and Evolutionary Computing
Electrical Engineering and Systems Science - Image and Video Processing
Computer Science Applications
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 378
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....3cc3e05cbe04d64b66401f452dee37d4