A Coupled Variational Autoencoder, which incorporates both a generalized loss function and latent layer distribution, shows improvement in the accuracy and robustness of generated replicas of MNIST numerals. The latent layer uses a Student's t-distribution to incorporate heavy-tail decay. The loss function uses a coupled logarithm, which increases the penalty on images with outlier likelihood. The generalized mean of the generated image's likelihood is used to measure the performance of the algorithm's decisiveness, accuracy, and robustness.

翻译：包含普遍损失函数和潜层分布的混合变式自动编码器显示,MNIST数字复制品的准确性和稳健性有所提高。 潜层使用学生的 t 分布法将重尾衰变纳入其中。 损失函数使用一种组合对数,这增加了图像受罚的可能性。 生成图像可能性的普遍平均值被用来测量算法的确定性、准确性和稳健性。