A generative deep learning model was developed to extract characteristics representing the inverse field of the permeability of a fluid in a porous medium, which in turn represents the runner system geometry of
Unlike classical (sparse, denoising, etc.) autoencoders, Variational autoencoders (VAEs) are generative models, like Generative Adversarial Networks 首先需要明确的是variational autocoder是一种典型的生成模型,而传统的autoencoder则不是。 与经典的autoencoder的不同的是,variational autoencoder通过encoder将inpu...
更强大的应用场景:随着Autoencoder和Variational Autoencoder的发展,它们将被应用于更多的领域,例如生成对抗网络(GANs)、图像生成和修复、自然语言处理(NLP)等。 解决模型解释性的挑战:Autoencoder和Variational Autoencoder的模型解释性可能受到限制,因为它们是基于深度学习的。因此,研究人员需要开发更好的模型解释性方法,以...
defforward(self,x):h=self.encoder(x)mu,log_var=h[:,:latent_dim],h[:,latent_dim:]z=self.reparameterize(mu,log_var)returnself.decoder(z),mu,log_var # 超参数设置 input_dim=784hidden_dim=400latent_dim=20batch_size=128learning_rate=1e-3num_epochs=50# 加载数据集 train_dataset=datasets...
A deep dive into Deep Learning Modeling- Advanced Neural Networks, incl. variational autoencoders Overview In this session, we will take a deeper dive into designing, customizing, and training advanced neural networks. We will demonstrate MATLAB's extended deep learning framewor...
Chapter 3. Variational Autoencoders Chapter Goals In this chapter you will: Learn how the architectural design of autoencoders makes them perfectly suited to generative modeling. Build and train an autoencoder … - Selection from Generative Deep Learnin
A variational autoencoder (VAE) is one of several generative models that use deep learning to generate new content, detect anomalies and remove noise. VAEs first appeared in 2013, about the same time as other generative AI algorithms, such as generative adversarial networks (GANs) and...
AE(Auto Encoder)、VAE(Variational AutoEncoder)、CVAE(Conditional AutoEncoder)解读,程序员大本营,技术文章内容聚合第一站。
In this paper, we introduce a model called Curiosity-driven Variational Autoencoder (CVAE), which combines variational autoencoder and curiosity-driven exploration. During the training process, the CVAE model can improve sample efficiency while curiosity-driven exploration can make sufficient exploration ...
The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics...