Auxiliary Autoencoder Beta-VAE VQ-VAE The core is vector quantization VQ-VAE2 introduce the multi-scale hierarchical organization TD-VAE Variational Lossy Autoencoder (VLAE) sample latent variables from simpl
Chen X, Kingma DP, Salimans T, Duan Y, Dhariwal P, Schulman J, Sutskever I, Abbeel P (2016) Variational lossy autoencoder. CoRR arXiv: 1611.02731 Van Den Oord A, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. In: International conference on machine learning, p...
Denoising Autoencoder:The goal is no longer to reconstruct the input data. Rather than adding a penalty to the loss function, we can obtain an autoencoder that learns something useful by changing the reconstruction error term of the loss function. This can be done by adding some noise to the...
VITS-Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech 论文原文:具有对抗性学习的条件变分自动编码器用于端到端文本到语音的转换 github:论文源码 摘要 最近提出了几种支持单阶段训练和并行采样的端到端文本转语音 (TTS) 模型,但它们的样本质量与两阶段 TTS 系统不匹配...
Lossy Image Compression with Normalizing Flows However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization. Due to that, they ... L Helminger...
Check out our project on github:https://github.com/compthree/variational-autoencoder. There, you can find our implementation of a variational autoencoder, all of the code that generated the results discussed here, and further details about network design and training that we did not discuss. We...
(e.g., representation learning, clustering, or lossy data compression) by introducing an objective function that allows practitioners to trade off between the information content ("bit rate") of the latent representation and the distortion of reconstructed data (Alemi et al., 2018). In this ...
Inside an auto encoder, there consists of several hidden layers 'h' which represents the input. Then clustering is undertaken and the result of clustering reflects. loss of information, so an Convolutional Neural network (CNN) lossy function is adopted to retrieve the lost information and finally...
A Variational Autoencoder is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Infere...
In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can ...