Troll Meme Classification for Tamil Memes Using Convolutional Neural Network

Abstract

Memes have become a new type of internet communication. It has the ability to instantly disseminate anger, offensiveness, and violence. Because of its regional meaning, classifying memes is difficult.. This work presents here a computational model of classifying Tamil memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. As a result, it can extract features automatically applying prior knowledge of existing categories, avoiding the time-consuming feature extraction process used in older methods in images. The basic layer of MobileNet is made up of depth-wise separable filters, also referred to as depth-wise separable convolution. The network structure is another feature that boosts performance. It utilizes very less than computation power while applying transfer learning. This network has reduced parameters and computation cost. Skip connections, or shortcuts, are used by residual neural networks to jump past some layers. Residual connections allow parameter gradients to travel more easily from the output layer to the network's prior layers, allowing for the training of deeper networks. Higher accuracies on more demanding tasks may arise from the greater network depth. AlexNet is a leading architecture for any image identification task, and it could have a lot of applications in the artificial intelligence field of computer vision. In the future, AlexNet may be used for image classification jobs more than CNNs. This work aims to classify Tamil memes using Mobilenet, Resnet and hyper parameter turned AlexNet.