What is GAN Architecture?

What is GAN Architecture?

Generative Adversarial Networks (GANs) are a powerful class of neural networks that are used for unsupervised learning. It was developed and introduced by Ian J. Goodfellow in 2014. It is a type of artificial intelligence (AI) model that consists of two neural networks: a generator and a discriminator. GANs are used for generative tasks, such as creating realistic images, videos, or even audio.

The generator network in a GAN generates synthetic data, such as images, based on random input or noise. Its goal is to generate samples that resemble the real data it was trained on. Initially, the generator produces low-quality samples, but as it learns, it improves its output.

The discriminator network acts as a judge and tries to distinguish between real and generated samples. It is trained on real data from a specific domain and learns to classify whether an input is real or fake. The discriminator provides feedback to the generator by indicating how well its generated samples resemble the real data. The generator adjusts its parameters weights based on this feedback, aiming to fool the discriminator by generating increasingly realistic samples.

The generator and discriminator are trained together in a competitive manner, where they both learn from each other. The generator learns to produce better samples, while the discriminator learns to become more accurate in distinguishing between real and fake data. This adversarial process continues until the generator becomes proficient at generating highly realistic samples that can fool the discriminator.

GANs have found applications in various domains, including computer vision, image synthesis, style transfer, text-to-image synthesis, and more. They have demonstrated impressive capabilities in generating highly realistic and creative content, making them a popular research area in AI.

GAN Paper Summary

#	GAN	Date	Architecture Type	Research Organization	Paper	Author Name
1	AAE Paper	2016	GAN	University of Montreal	Adversarial Autoencoder	Alireza Makhzani et al.
2	cGANs Paper	2014	GAN	University of Montreal	Conditional GAN	Mehdi Mirza and Simon Osindero
3	CycleGAN Paper	2017	GAN	University of California, Berkeley	Cycle-Consistent GAN	Jun-Yan Zhu et al.
4	DCGAN Paper	2015	GAN	OpenAI	Deep Convolutional GAN	Alec Radford et al.
5	DiscoGAN Paper	2017	GAN	Seoul National University	DiscoGAN	Taeksoo Kim et al.
6	EGAN Paper	2018	GAN	The Chinese University of Hong Kong	Energy-Based GAN	Zhaoxin Li et al.
7	GAN Paper	2014	GAN	University of Montreal	Generative Adversarial Network	Ian Goodfellow et al.
8	IsGAN Paper	2017	GAN	Carnegie Mellon University	Improved-Synthesis GAN	Zhiting Hu et al.
9	Large Scale GAN Paper	2018	GAN	University of Edinburgh	Large Scale GAN Training for High Fidelity Natural Image Synthesis	Andrew Brock et al.
10	LSGAN Paper	2017	GAN	University of California, Berkeley	Least Squares GAN	Xudong Mao et al.
11	PGAN Paper	2017	GAN	NVIDIA	Progressive Growing of GANs	Tero Karras et al.
12	pixelRNN Paper	2016	GAN	Google DeepMind	Pixel Recurrent Neural Networks	Aaron van den Oord et al.
13	StackGAN Paper	2017	GAN	Carnegie Mellon University	StackGAN	Han Zhang et al.
14	StyleGAN Paper	2019	GAN	NVIDIA	StyleGAN	Tero Karras et al.
15	text-to-image Paper	2016	GAN	University of Michigan	Generative Adversarial Text-to-Image Synthesis	Scott Reed et al.
16	WGAN Paper	2017	GAN	New York University	Wasserstein GAN	Martin Arjovsky et al.

GAN Capabilities

#	GAN	Objective	Summary	NLP Tasks	CV Tasks
1	AAE	Adversarial Autoencoder	A type of autoencoder that combines generative and discriminative models through an adversarial process.	-	Image Generation
2	cGANs	Conditional Generative Adversarial Networks	A generative model that can generate samples conditioned on specific input conditions or labels.	-	Image Generation, Image-to-Image Translation
3	CycleGAN	Cycle-Consistent Generative Adversarial Network	A model for image-to-image translation that learns mappings between two domains without paired training data.	-	Image-to-Image Translation
4	DCGAN	Deep Convolutional Generative Adversarial Network	A deep convolutional neural network architecture for training generative models using GANs.	-	Image Generation
5	DiscoGAN	Discover Cross-Domain Relations with GANs	A GAN-based model that learns to map images between different domains without paired training data.	-	Image-to-Image Translation
6	EGAN	Energy-Based Generative Adversarial Network	A generative model that assigns an energy score to each sample and generates samples with low energy.	-	Image Generation
7	GAN	Generative Adversarial Network	A framework that consists of a generator and a discriminator network that compete in a two-player min-max game.	-	Image Generation
8	IsGAN	Improved Wasserstein GAN	A variation of the Wasserstein GAN that improves stability and convergence during training.	-	Image Generation
9	Large Scale GAN	Large Scale Generative Adversarial Network	GAN models that are designed for generating high-resolution and complex images.	-	Image Generation
10	LSGAN	Least Squares Generative Adversarial Network	A GAN variant that uses least squares loss functions to improve the training stability and reduce mode collapse.	-	Image Generation
11	PGAN	Progressive Growing of GANs	A training technique for GANs that gradually increases the size of generated images during training.	-	Image Generation
12	pixelRNN	Pixel Recurrent Neural Network	A generative model that generates images pixel by pixel using recurrent neural networks.	-	Image Generation
13	StackGAN	Stack Generative Adversarial Networks	A model that generates high-resolution images in a two-step process, first generating low-resolution images and then refining them.	-	Image Generation
14	StyleGAN	Style-Based Generative Adversarial Network	A GAN architecture that uses a learned latent space to control the style and appearance of generated images.	-	Image Generation
15	text-to-image	Text-to-Image Synthesis	Models that generate images from textual descriptions or captions.	Text Generation, Image Generation	Image Generation
16	WGAN	Wasserstein Generative Adversarial Network	A GAN variant that uses Wasserstein distance as a loss function to improve training stability.	-	Image Generation