This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch. The resulting branched VAE (BVAE) contributes a classification component based on the class labels to the total loss and therefore imparts categorical information to the latent representation. As a result, the latent space distributions of the input classes are separated and ordered, thereby enhancing the classification accuracy. The degree of improvement is quantified by numerical calculations employing the benchmark MNIST dataset for both unrotated and rotated digits. The proposed technique is then compared to and then incorporated into a VAE with fixed output distributions. This procedure is found to yield improved performance for a wide range of output distributions.

翻译：本文提出了一种改进的变分自编码器（VAE），其包含一个额外的神经网络分支。由此产生的分支型VAE（BVAE）基于类别标签向总损失中引入分类分量，从而将类别信息赋予潜在表示。这使得输入类别的潜在空间分布得以分离并有序排列，进而提升了分类准确率。通过采用基准MNIST数据集对未旋转和旋转数字进行数值计算，量化了改进程度。随后将所提技术与具有固定输出分布的VAE进行对比并将其整合到后者中，实验表明，该方法在多种输出分布下均能获得更优的性能。