Manifold Preserving CNN for Pixel-Based Object Labelling in Images for High Dimensional Feature spaces

Srivastava, Vishal; Biswas, Bhaskar

doi:10.1007/s11063-020-10415-4

Cited by 3 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature learning has several basic parts, such as the convolutional and pooling layers. Although, there are still many layers that can be applied to feature learning [82]. The application of several layers in feature learning can generally be discussed in these two sections, including:…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

CNN Ensemble Learning Method for Transfer learning: A Review

Sulistya,

Br Bangun,

Tyas

2023

Ilk. J. Ilm.

View full text Add to dashboard Cite

This study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3,

show abstract

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%