变分自编码器层

AI总结：

提出将变分自编码器作为神经网络层集成，并设计新训练策略，分析其性能。

摘要：

变分自编码器（VAE）属于具有概率特性的自编码器家族，通过生成平滑连续的潜在空间，非常适合数据生成。尽管已提出十多年，该方法仍在研究和工业中被广泛采用。虽然VAE通常作为独立模型使用，本文提出了一种将其作为神经网络层集成的新方法。此外，针对包含这些层的模型，提出了一种新的训练策略，并对其性能进行了深入分析。

英文摘要：

Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications. While VAEs are typically used as standalone models, this paper introduces a novel approach to integrate them as a neural network layer. Furthermore, a new training strategy is proposed for models incorporating these layers, and their performance is thoroughly analyzed.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。