Laplacian Pyramid Network
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What is LapStyle?
LapStyle, also known as Laplacian Pyramid Network, is a method for transferring styles from one image to…
Prescribed Generative Adversarial Network
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What is PresGAN?
PresGAN, short for Prescribed Generative Adversarial Networks, is a type of machine learning algorithm that is used…
StyleGAN2
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What is StyleGAN2?
StyleGAN2 is a type of artificial intelligence technology known as a generative adversarial network. It is an…
Vision-aided GAN
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In recent years, computer scientists have been working on improving the performance of Generative Adversarial Networks (GANs), which are machine…
BigBiGAN
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BigBiGAN is a type of machine learning algorithm that generates images. It is a combination of two other algorithms called…
DVD-GAN
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DVD-GAN is a type of artificial intelligence that can create video. It uses a system called a generative adversarial network,…
ControlVAE
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ControlVAE is a system that combines two different technologies to help improve the efficiency of machine learning algorithms. It is…
Nouveau VAE
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NVAE: A Deep Hierarchical Variational Autoencoder
NVAE, or Nouveau VAE, is a powerful deep learning algorithm designed to address the…
TrIVD-GAN
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TrIVD-GAN, or Transformation-based & TrIple Video Discriminator GAN, is a cutting-edge technology in the field of video generation that builds upon…
Viewmaker Network
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What is Viewmaker Network?
Viewmaker Network is a type of generative model that learns to produce input-dependent views for contrastive…
Sliced Iterative Generator
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The Sliced Iterative Generator (SIG) is an advanced generative model that employs a Normalizing Flow and Generative Adversarial Networks techniques…
Adversarial Latent Autoencoder
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ALAE, or Adversarial Latent Autoencoder, is an innovative type of autoencoder used to tackle some of the limitations of generative…
Topographic VAE
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Overview of Topographic VAE
Topographic VAE is a method used for training deep generative models with topographically organized latent variables.…
StyleALAE
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StyleALAE is a cutting-edge technique used in machine learning that incorporates the concept of adversarial latent autoencoders with StyleGAN. By…
Informative Sample Mining Network
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If you've ever used a computer for a long time, you might have noticed a lot of images and videos…
Hierarchical Style Disentanglement
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Image-to-image translation models have been a topic of interest in the field of machine learning for several years. These models…
Attribute2Font
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Overview of Attribute2Font
Attribute2Font is a computer model that can be used to create fonts by synthesizing visually pleasing glyph…
LOGAN
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The topic of LOGAN pertains to the use of deep learning techniques to generate high-quality images. Specifically, LOGAN is a…
CS-GAN
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CS-GAN is a type of generative adversarial network that is used to improve the quality of generated samples. This is…
Regularized Autoencoders
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An autoencoder is a type of neural network that is trained to learn a compressed representation of data, typically for…
Beta-VAE
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Beta-VAE is a type of machine learning model known as a variational autoencoder (VAE). The goal of Beta-VAE is to…
StyleGAN
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StyleGAN: An Overview of the Generative Adversarial Network
StyleGAN is a type of generative adversarial network (GAN) used for generating…
BigGAN
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Introduction to BigGAN
BigGAN is a type of generative adversarial network that uses machine learning to create high-resolution images. It…
BigGAN-deep
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BigGAN-deep is a deep learning model that builds on the success of BigGAN by increasing the network depth four times.…
GLOW
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GLOW is a powerful generative model that is based on an invertible $1 \times 1$ convolution. This innovative model is…
k-Sparse Autoencoder
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What is a k-Sparse Autoencoder?
A k-Sparse Autoencoder is a type of neural network that achieves sparsity in the hidden…
Contractive Autoencoder
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Introduction to Contractive Autoencoder
A **Contractive Autoencoder** is a type of neural network that learns how to compress data into…
Non-linear Independent Component Estimation
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The Non-Linear Independent Components Estimation (NICE) framework is a powerful tool for understanding high-dimensional data. It's based on the idea…
Deep Boltzmann Machine
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A Deep Boltzmann Machine (DBM) is a type of generative model used in deep learning. It is similar to a…
Introspective Adversarial Network
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Introspective Adversarial Network (IAN) is a unique combination of two deep learning techniques – Generative Adversarial Networks (GANs) and Variational Autoencoders…
Adversarially Learned Inference
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What is ALI?
Adversarially Learned Inference (ALI) is an approach for generative modelling that has gained attention in the field…
RealNVP
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RealNVP: A Generative Model for Density Estimation
What is RealNVP?
RealNVP is a generative model that utilizes real-valued non-volume preserving…
Pixel Recurrent Neural Network
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PixelRNNs are a type of neural network that can create realistic images by predicting the pixels in an image pixel…
Stacked Denoising Autoencoder
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The Stacked Denoising Autoencoder (SDAE) is a type of deep learning model used for unsupervised pre-training and supervised fine-tuning. As…
TGAN
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TGAN: A Revolutionary Generative Adversarial Network
Generative adversarial networks, or GANs, have been used to produce high-quality images and videos.…
Bidirectional GAN
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BiGAN, which stands for Bidirectional Generative Adversarial Network, is a type of machine learning model used in unsupervised learning. It…
InfoGAN
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Introduction to InfoGAN
InfoGAN is a type of generative adversarial network (GAN) which is used to learn interpretable and meaningful…
Spectrally Normalised GAN
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Overview of SNGAN:
SNGAN, or Spectrally Normalised GAN, is a powerful type of generative adversarial network that can be used…
Progressively Growing GAN
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What is ProGAN?
ProGAN stands for Progressively Growing GAN, which is a type of machine learning algorithm. Specifically, it is…
PixelCNN
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PixelCNN is a type of computer model that is used to create images by breaking them down into individual pixels.…
Sparse Autoencoder
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A sparse autoencoder is a popular type of neural network that uses sparsity as a way to compress information. The…
Pix2Pix
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Pix2Pix: A Revolutionary Image-to-Image Translation Architecture
Have you ever wanted to see how a color photograph would look as a…
LAPGAN
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Generative Adversarial Networks or GANs are deep learning models that can learn to generate realistic images from random noise. However,…
Denoising Autoencoder
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Have you ever wondered how computers can recognize images or detect patterns? A Denoising Autoencoder (DAE) is a type of…
High-resolution Deep Convolutional Generative Adversarial Networks
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HDCGAN, also known as High-resolution Deep Convolutional Generative Adversarial Networks, is a powerful technology for generating high-quality images. This architecture…
Deep Belief Network
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Understanding Deep Belief Networks (DBN)
Deep Belief Networks (DBN) are a type of multi-layer generative graphical models that are heavily…
VQ-VAE
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A VQ-VAE is a type of variational autoencoder that is able to obtain a discrete latent representation for data. It…
Restricted Boltzmann Machine
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Restricted Boltzmann Machines
Restricted Boltzmann Machines, or RBMs, are types of neural networks that can learn to represent probability distributions…
LSGAN
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LSGAN: An Introduction to the Least Squares Generative Adversarial Network
Generative adversarial networks (GANs) have revolutionized the field of artificial…
Variational Autoencoder
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A Variational Autoencoder, or VAE, is a type of computer program that creates new data based on existing data. This…