Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and the decoder/generator while ensuing convergence. Most existing auto-encoders cannot automatically trade off bidirectional mapping. In this work, we propose Bi-GAE, an unsupervised bidirectional generative auto-encoder based on bidirectional generative adversarial network (BiGAN). First, we introduce two terms that enhance information expansion in decoding to follow human visual models and to improve semantic-relevant feature representation capability in encoding. Furthermore, we embed a generative adversarial network (GAN) to improve representation while ensuring convergence. The experimental results show that Bi-GAE achieves competitive results in both generation and representation with stable convergence. Compared with its counterparts, the representational power of Bi-GAE improves the classification accuracy of high-resolution images by about 8.09%. In addition, Bi-GAE increases structural similarity index measure (SSIM) by 0.045, and decreases Fréchet inception distance (FID) by 2.48 in the reconstruction of 512 × 512 images.

提高自动编码器的生成和表示能力是一个热门研究课题。然而，在确保收敛的同时联合并同时优化编码器和解码器/生成器之间的双向映射是一个挑战。大多数现有的自动编码器无法自动权衡双向映射。在这项工作中，我们提出了 Bi-GAE，一种基于双向生成对抗网络（BiGAN）的无监督双向生成自动编码器。首先，我们引入两个术语，它们可以增强解码中的信息扩展以遵循人类视觉模型，并提高编码中的语义相关特征表示能力。此外，我们嵌入了生成对抗网络（GAN）来改善表示，同时确保收敛。实验结果表明，Bi-GAE 在生成和表示方面都取得了有竞争力的结果，并且收敛稳定。与同类产品相比，Bi-GAE 的表征能力将高分辨率图像的分类精度提高了约 8.09%。此外，在 512 × 512 图像的重建中，Bi-GAE 将结构相似性指数测量（SSIM）提高了 0.045，并将 Fréchet 起始距离（FID）降低了 2.48。