Transfer Learning for Feature Dimensionality Reduction
Abstract: Transfer learning is a machine learning methodology by which a model developed for achieving a task is exploited for another related job. Many pre-trained image classification models trained on ImageNet are used for transfer learning. These pre-trained networks could also be used for classifying out of domain images by retraining them. This paper, along with the existing application for these pre-trained models, is also being exploited for feature dimensionality reduction. Many dimensionality reduction methods are available; the pre-trained image models will help us perform both image feature extraction and dimensionality reduction in a single go using the same network. The fine-tuning of the fully connected layers of the pre-trained network is done to extract the image features; along with this fine-tuning, some more tweaking is done on the fully connected layers of these models to reduce the image feature dimensionality. Here, VGG-16 and VGG-19 are the pre-trained models considered for feature vector generation and dimensionality reduction. An analysis of the efficiency of features generated by these pre-trained networks in classifying the out-of-domain images is done. Three different variants of VGG-16 and VGG-19 are analysed. All the three variants developed gave an AUC value above 0.8, which is considered good.
Keywords: Dimensionality reduction, fine-tuning, transfer learning, VGG-16, VGG-19.
Received August 10, 2020; accepted October 13, 2021