Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis

Table 1

List of abbreviations used in this manuscript along with their expansion.

Abbreviation	Full form	Authors
CV	Computer vision	Roberts, 1963 [1]
D-CNN	Deep convolutional neural network	Lecun et al., 1998 [2]
RNN	Recurrent neural network	Graves, 2006 [3]
DNN	Deep neural network	Ivakhnenko, 1971 [4]
AI	Artificial intelligence	John McCarthy, 1956
MNIST	Mixed National Institute of Standards and Technology	Lecun et al. (http://yann.lecun.com/exdb/mnist/)
ReLU	Rectified linear unit	Hahnloser et al., 2000 [5]
COCO	Common objects in context	Lin et al, 2014 [6]
D-GAN	Deep generative adversarial network	Goodfellow et al, 2014 [7]
DCGAN	Deep convolutional GAN	Radford et al., 2015 [8]
SRGAN	Super-resolution generative adversarial networks	Ledig et al., 2017 [9]
APGAN	Laplacian pyramid GAN	Denton et al., 2015 [10]
SAPGAN	Self-attention generative adversarial networks	Zhang, et al., 2019 [11]
GRAN	Generating images with recurrent adversarial networks	Im, et al., 2016 [12]
GPF-CNN	Gated peripheral-foveal convolutional neural network	Hahnloser et al., 2000 [5]
PSGAN	Pose and expression robust spatial-aware GAN	Jiang et al., 2019 [13]
ResNet	Residual neural network	He Zhang et al., 2016 [28]
CRGAN	Conditional recycle GAN	Li et al., 2018 [14]
ACGAN	Auxiliary classifier GAN	Odena et al, 2017 [15]
CGAN	Conditional GAN	Gauthier et al., 2014 [16]
InfoGAN	Information maximizing GAN	Chen et al., 2016 [17]
LAPGAN	Laplacian pyramid of adversarial networks	Denton et al., 2015 [10]
SAGAN	Self-attention GAN	Zhang et al., 2018 [11]
VAEGAN	Variational autoencoder GAN	Larsen et al., 2016 [12]
BIGAN	Bidirectional GAN	Rui et al., 2020 [18]
AAE	Adversarial autoencoders	Makhzani et al., 2016 [19]
MCGAN	Mean and covariance feature matching GAN	Mroueh et al., 2017 [20]
GRAN	Generative recurrent adversarial networks	Daniel et al., 2016 [12]
LSGAN	Least squares generative adversarial networks	Mao et al., 2016 [29]
WGAN	Wasserstein GAN	Martin et al., 2017 [21]