Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks

Denoising Criterion for Variational Auto-Encoding Framework

Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injected in their stochastic hidden layer, with a regularizer that encourages this noise injection. In this paper, we show that injecting noise both in input and in the stochastic hidden layer can be advantageous and we propose a modified variational lower bound as an improved objective function in this setup. When input is corrupted, then the standard VAE lower bound involves marginalizing the encoder conditional distrib…

How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model

Energy-based models parameterize the unnormalized log-probability of data samples, but there is a lack of guidance on how to construct the "energy". In this paper, we propose a Denoising-EBM which decomposes the image energy into "semantic energy" and "texture energy". We define the "semantic energy" in the latent space of DAE to model the high-level representations, and define the pixel-level reconstruction error for denoising as "texture energy". Inspired by score-based model, our model utilizes multi-scale noisy samples for maximum-likelihood training and it outputs a vector instead of a sc…

Cascading Denoising Auto-Encoder as a Deep Directed Generative Model

Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE) become gener-ative models as a density estimator. However,in practice, the framework suffers from a mixingproblem in the MCMC sampling process and nodirect method to estimate the test log-likelihood.We consider a directed model with an stochas-tic identity mapping (simple corruption pro-cess) as an inference model and a DAE as agenerative model. By cascading these mod-els, we propose Cascading Denoising Auto-Encoders(CDAE) which can generate samples ofdata distribution from tractable prior distributionunder the assumpt…

结合去噪扩散概率模型的分子图变分自编码

Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model

针对常规VAE潜变量后验分布假设简单限制表征性能的问题，提出结合DDPM的层级潜变量分子生成模型，在小数据集分子性质预测任务上实现更优性能与鲁棒性。

用于无线语义通信的条件去噪扩散自编码器

Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications

本文提出条件去噪扩散自编码器用于无线语义通信，通过语义编码与条件扩散解码学习从语义空间到真实值分布的映射，并在CIFAR-10、MNIST及多用户场景中验证其优于传统自编码器和VAE的性能。

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what can be restored in corrupted images, and like for all inverse problems, many potential solutions exist, and one of them must be chosen. Here, we propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs), overcoming the problem of havin…