We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an "inverse mapping" that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, FAAE minimizes re-encoding errors in the latent space and exploits adversarial criterion in the data space. Experimental evaluations demonstrate that the proposed framework produces sharper reconstructed images while at the same time enabling inference that captures rich semantic representation of data.

翻译：我们建议一个翻转自动编码器(FAAE)同时培训一个基因模型G,该模型将任意的潜在代码分布映射成数据分布图和一个编码器E,其中包含一个“反向映射”将数据样本编码成潜在代码矢量的“反向映射 ” 。 与以前利用对抗性培训标准构建自动编码器的混合方法不同,FAE将潜在空间的重新编码错误减少到最低程度,并在数据空间中利用对立标准。 实验评估表明,拟议框架生成了更清晰的重建图像,同时能够推断能够捕捉到丰富的数据语义代表。