基于深度度量变分自编码器的多模态数据生成

Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their self-organized variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the vari…

Multiple Style Transfer via Variational AutoEncoder

Modern works on style transfer focus on transferring style from a single image. Recently, some approaches study multiple style transfer; these, however, are either too slow or fail to mix multiple styles. We propose ST-VAE, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, enabling to merge styles via linear interpolation before transferring the new style to the content image. To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. We also present a case study r…

用于可解释心脏病分类的属性正则化软自省变分自编码器

Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification

本研究针对医学影像AI可解释性需求，提出结合潜空间属性正则化的对抗训练变分自编码器方法，可解决重建模糊问题，提升心脏病分类的可解释性。

使用几何变分自编码器建模巴雷特食管进展

Modeling Barrett's Esophagus Progression using Geometric Variational Autoencoders

针对巴雷特食管早期检测需求，研究几何变分自编码器捕捉其进展潜在表示的效果，验证两类几何VAE的性能提升，并提出兼具生成能力与自编码器优势的新型架构。

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper

Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detecting anomalies in medical images. However, especially Variational Autoencoders (VAEs)often fail to capture the high-level structure in the data. We address these shortcomings by proposing the context-encoding Variational Autoencoder (ceVAE), which improves both, the sample, as well as pixelwise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks the ceVAE achieves unsupervised AUROCs o…

Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study

We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neuroradiologists' evaluations and diverse metrics with respect to quality and diversity of the generated synthetic brain images, we present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field, where information is saved in a dispatched and inhomogeneous way and access to it is in many aspects restricted.