Search Results for author:
Found 6 papers, 0 papers with code
Facial Information Recovery from Heavily Damaged Images using Generative Adversarial Network- PART 1
Hence, it is necessary to develop an intellectual framework to recover the possible information presented in the original scene.
Learning The Sequential Temporal Information with Recurrent Neural Networks
Recurrent Networks are one of the most powerful and promising artificial neural network algorithms to processing the sequential data such as natural languages, sound, time series data.
Implementation of Deep Convolutional Neural Network in Multi-class Categorical Image Classification
Also, the complex architecture requires a significant amount of data to train and involves with a large number of hyperparameters that increases the computational expenses and difficul- ties.
Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior
Reportedly, Gird search and Random search are said to be inefficient and extremely expensive, due to a large number of hyperparameters of the architecture.
Regularization and Optimization strategies in Deep Convolutional Neural Network
Convolution Neural Networks, known as ConvNets exceptionally perform well in many complex machine learning tasks.
Feed Forward and Backward Run in Deep Convolution Neural Network
After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution Neural Network is attracted many researchers.
