We use a Convolutional Neural Network trained on a source dataset (with lots of data) to project the data of a target dataset (with limited data) onto another ...
This work investigates a method to transfer learning across different texture classification problems, using CNNs, in order to take advantage of this type ...
Jul 28, 2015 · We use a Convolutional Neural Network trained on a source dataset (with lots of data) to project the data of a target dataset (with limited data) ...
Dec 10, 2022 · Transfer learning helps leverage the knowledge learnt by a model on one data set to extract information on another data set. Transfer learning ...
This paper aims to improve defect detection and classification accuracy using a new approach that combines part of a pre-trained VGG16 model as a feature ...
Transfer learning between texture classification tasks using Convolutional Neural Networks. L. G. Hafemann, Luiz Oliveira, P. Cavalin, R. Sabourin. 2015, IEEE ...
May 6, 2020 · The objective of this study is to increase the classification accuracy, speed the training time and avoid the overfitting. In this work, we ...
People also ask
What is transfer learning in Convolutional Neural Network?
What is texture classification using convolutional neural networks?
Why are convolutional neural networks CNN particularly well suited for image classification tasks?
How to use Convolutional Neural Network for image classification?
CNN is useful not only in the image recognition but also in the textural feature representation. Texture features, which are automatically learned and extracted ...
Jul 25, 2022 · The results for the texture classification task can be improved when the texture extraction feature technique is added along with existing CNNs.
May 31, 2019 · In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA.