Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder

Understand

We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data.

• The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling.
• We evaluate the performance of the coupled VAE model using the MNIST dataset.
• Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry.