Inspect Transfer Learning Architecture with Dilated Convolution

-- There are many award - winning pre - trained Convolutional Neural Network (CNN), which have a common phenomen on of increasing depth in convolutional layers. However, I inspect on VGG network, which is one of the famous model submitted to ILSVRC - 2014, to show that slight modification in the basic architecture can enhance the accuracy result of the image classification task. In this paper, We present two improv e architectures of pre - trained VGG - 16 and VGG - 19 networks that appl y transfer learning when trained on a different dataset. I report a series of experimental result on various modification of the primary VGG networks and achieved sign ificant out - performance on image classification task by: (1) freezing the first two blocks of the convolutional layers to prevent over - fitting and (2) applying different combination of dilation rate in the last three blocks of convolutional layer to reduce image resolution for feature extraction. Both the proposed architecture achieve s a competitive result on CIFAR - 10 and CIFAR - 100 dataset. Keywords -- CNN, VGG - 16, VGG - 19, Dilated Convolution, transfer learning I. INTRODUCTION Convolutional networks (ConvNets) have achieved excellent success in the large - scale image and video recognition, which has become feasible before large public image repositories such as ImageNet [1] and high - performance computi ng system s such as GPUs or large - scale distributed clusters. These advancements were largely motivated by strong baseline schema s, such as semantic segmentation [2], object recognition [3], image capt ioning [4], and human pose estimation[4].