Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
This work addresses the need for more reliable probabilistic inferences in generative models, but it is incremental as it builds on existing VAE frameworks with specific modifications.
The paper tackled the problem of improving accuracy and robustness in variational autoencoders by introducing a coupled VAE method that models dependencies and penalizes outliers, resulting in clearer generated images and improved neutral and robust accuracy metrics on the MNIST dataset.
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. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and a more compact latent space generates the output values. We analyze the histogram of the likelihoods of the input images using the generalized mean, which measures the model's accuracy as a function of the relative risk. The neutral accuracy, which is the geometric mean and is consistent with a measure of the Shannon cross-entropy, is improved. The robust accuracy, measured by the -2/3 generalized mean, is also improved.