Abstract
Variational Autoencoders (VAEs) have become a recognized tool for learning data distributions in various applications. Building on previous research, this paper investigates image reconstruction by incorporating Planar Flows into VAEs. As a form of Normalizing Flows, Planar Flows potentially aid VAEs in modeling more intricate distributions, which may enhance the quality of reconstructed images. Our study employs a well-established architecture that integrates Planar Flows with VAEs. Experiments on MNIST are provided to compare our replication efforts with traditional VAEs. The results suggest that utilizing Planar Flows can offer certain improvements in image reconstruction. This work further solidifies the understanding of the combined use of VAEs and Planar Flows in image reconstruction tasks.
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