In image generation problem for example, we have no concrete target vector. Generative models have been proven useful for solving this kind of issues. In this paper, we will compare two types of generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We ...
Variational-Auto-Encoder for tooth image generation 来自 Semantic Scholar 喜欢 0 阅读量: 3 作者: G Zhu 摘要: Variational Autoencoder (VAE), as a kind of deep latent space generation model, has achieved great success in performance in recent years, especially in image generation. In this ...
Indeed, nothing in the task the autoencoder is trained for enforce to get such organisation: the autoencoder is solely trained to encode and decode with as few loss as possible, no matter how the latent space is organised. Thus, if we are not careful about the definition of the architectur...
In this study, we build a variational autoencoder (VAE) with SNN to enable image generation. VAE is known for its stability among generative models; recently, its quality advanced. In vanilla VAE, the latent space is represented as a normal distribution, and floating-point calculations are ...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...
Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they...
Lecture 4 Latent Variable Models -- Variational AutoEncoder (VAE) While the old way of doing statistics used to be mostly concerned with inferring what has happened, modern statistics is more concerned with predicting what will happen, and many practical machine learning applications rely on it. ...
摘要原文 In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibl...
TensorFlow implementation of Deep Convolutional Generative Adversarial Networks, Variational Autoencoder (also Deep and Convolutional) and DRAW: A Recurrent Neural Network For Image Generation. Run VAE/GAN: python main.py --working_directory /tmp/gan --model vae DRAW: python main-draw.py --working...
Unlike traditional autoencoders [35], which only compress the image to a point in the latent space, variational autoencoders force the latent variables of the latent space to a standard normal distribution. The encoder is no longer given a point but a distribution, allowing the model to ...