Vijayan K. Asari

TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).

TL;DR: A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.

TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

TL;DR: The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression and is expected to be able to cope with these variations.

TL;DR: The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U- net.