How do Variational Autoencoders Learn? Insights from Representational Similarity
This provides incremental insights into VAE learning dynamics, aiding researchers in understanding model behavior and addressing issues like posterior collapse.
The paper investigates the internal behavior of Variational Autoencoders (VAEs) using representational similarity techniques, finding that encoder representations are learned earlier than decoder ones and are consistent across various conditions.
The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully understood. For example, the questions of when they can provide disentangled representations, or suffer from posterior collapse are still areas of active research. Despite this, there are no layerwise comparisons of the representations learned by VAEs, which would further our understanding of these models. In this paper, we thus look into the internal behaviour of VAEs using representational similarity techniques. Specifically, using the CKA and Procrustes similarities, we found that the encoders' representations are learned long before the decoders', and this behaviour is independent of hyperparameters, learning objectives, and datasets. Moreover, the encoders' representations in all but the mean and variance layers are similar across hyperparameters and learning objectives.