标题： 基于（$β$-）变分自编码器和生成对抗网络的解缠表示学习 显示英文标题

标题： Disentangled Representation Learning Using ($β$-)VAE and GAN

摘要： 给定一个包含具有不同特征（如形状、大小、旋转和平面位置）的不同物体的图像数据集，以及一个变分自编码器（VAE）；本文感兴趣的任务是在该VAE的隐空间向量中创建这些特征的解纠缠编码。dSprite 数据集为本研究所需的实验提供了所需特征。 在训练结合了生成对抗网络（GAN）的VAE之后，对隐向量的每个维度进行了扰动，以探索每个维度中的解纠缠现象。请注意，GAN用于提高输出图像重建的质量。 显示英文摘要

摘要： Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provided the desired features for the required experiments in this research. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension. Note that the GAN was used to improve the quality of output image reconstruction.