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Research Topics in Convolutional Neural Networks

Research Topics in Convolutional Neural Networks

   Convolutional Neural Networks (CNN) is a type of Neural Networks, and it has multiple feature extraction stages that can automatically learn representations from the data. CNN is composed of three layers include convolution layer, pooling layer, and fully connected layer. The convolution layer performs a mathematical operation on two functions that produce a third function. Also, it performs feature extraction, and the pooling layer performs more complex feature extraction, and the fully connected layer maps the extracted feature into the final output, i.e., classification. Each layer generates several activation functions that are passed on to the next layer. As one layer feeds its output into the next layer, extracted features can hierarchically and progressively become more complex. The most popular applications of CNN are Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition.

   • In the Deep learning field, a Convolutional neural network (CNN) is a representative algorithm that has the ability to extract features from data with convolution structures.

   • Deep convolutional networks strained end–to–end to capture low- and high-level features on every convolutional layer.

   • Because of its multi-building blocks, it possesses some advantages such as local connections, weight sharing, and down-sampling dimensionality reduction and has become the new favorite method in the deep learning field.

   • Through the fully connected layer, CNN can harness a massive amount of data, extracts the implicit characteristics, and achieves better classification performance than the other traditional machine learning models.

   • Moreover, CNN can harness different activation functions to express complex features and greatly enhances the ability of neural networks to fit data.

   • In recent years, CNN has made brilliant achievements in immense areas involving applications of 2-D CNN, object detection, image-related tasks, and many others.

   • Even though CNN performs a well-suited model for solving problems in the spatial dimension, it fails to handle data in the temporal dimension.

   • Furthermore, there are many problems that convolution is hard to handle, such as low generalization ability, lack of equivariance, and poor crowded-scene results.

   • CNN has proven its superiority as it lessens the problems in natural language processing tasks.

   • AlexNet, VGGNet, GoogLeNet & Inception, ResNet, DenseNet, MobileNets, RegNet and GhostNet are the most innovative developments in CNN.