  Deep Convolutional Neural Networks (CNN) have evolved as popular machine
learning models for image classification during the past few years, due to
their ability to learn the problem-specific features directly from the input
images. The success of deep learning models solicits architecture engineering
rather than hand-engineering the features. However, designing state-of-the-art
CNN for a given task remains a non-trivial and challenging task, especially
when training data size is less. To address this phenomena, transfer learning
has been used as a popularly adopted technique. While transferring the learned
knowledge from one task to another, fine-tuning with the target-dependent Fully
Connected (FC) layers generally produces better results over the target task.
In this paper, the proposed AutoFCL model attempts to learn the structure of FC
layers of a CNN automatically using Bayesian optimization. To evaluate the
performance of the proposed AutoFCL, we utilize five pre-trained CNN models
such as VGG-16, ResNet, DenseNet, MobileNet, and NASNetMobile. The experiments
are conducted on three benchmark datasets, namely CalTech-101, Oxford-102
Flowers, and UC Merced Land Use datasets. Fine-tuning the newly learned
(target-dependent) FC layers leads to state-of-the-art performance, according
to the experiments carried out in this research. The proposed AutoFCL method
outperforms the existing methods over CalTech-101 and Oxford-102 Flowers
datasets by achieving the accuracy of 94.38% and 98.89%, respectively. However,
our method achieves comparable performance on the UC Merced Land Use dataset
with 96.83% accuracy. The source codes of this research are available at
https://github.com/shabbeersh/AutoFCL.
