We propose a generative model termed Deciphering Autoencoders. In this model, we assign a unique random dropout pattern to each data point in the training dataset and then train an autoencoder to reconstruct the corresponding data point using this pattern as information to be encoded. Since the training of Deciphering Autoencoders relies solely on reconstruction error, it offers more stable training than other generative models. Despite its simplicity, Deciphering Autoencoders show comparable sampling quality to DCGAN on the CIFAR-10 dataset.

翻译：我们提出了一种名为“解码自编码器”的生成式模型。在该模型中，我们为训练数据集中的每个数据点分配一个唯一的随机丢弃模式，然后训练一个自编码器，利用该模式作为待编码信息来重建对应的数据点。由于解码自编码器的训练仅依赖于重构误差，其训练过程比其他生成式模型更加稳定。尽管结构简单，解码自编码器在CIFAR-10数据集上表现出与DCGAN相当的采样质量。