论文标题
使用（$β$ - ）vae和gan解开表示的学习
Disentangled Representation Learning Using ($β$-)VAE and GAN
论文作者
论文摘要
给定包含具有不同特征的不同对象的图像数据集，例如形状，大小，旋转和X-y位置；以及变异自动编码器（VAE）；在VAE的隐藏空间向量中创建这些功能的分解编码是本文感兴趣的任务。 DSPRITE数据集为这项研究中所需的实验提供了所需的功能。在训练VAE与生成对抗网络（GAN）结合后，隐藏矢量的每个维度都被中断以探索每个维度中的分离。请注意，GAN用于提高输出图像重建的质量。
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.
