From KL Divergence to Wasserstein Distance: Enhancing Autoencoders with FID Analysis

Variational Autoencoders (VAEs) are popular Bayesianinference models that excel at approximating complexdata distributions in a lower-dimensional latent space.Despite their widespread use, VAEs frequently facechallenges in image generation, often resulting in blurryoutputs. This outcome is primarily attributed to twofactors: the inherent probabilistic nature of the VAEframework and the oversmoothing effect induced bythe Kullback-Leibler (KL) divergence term in the lossfunction. This paper explores the integration of Wasser-stein Distance into the VAEs framework, resulting ina Wasserstein Autoencoders (WAEs) designed to mit-igate the oversmoothing issue and enhance the qual-ity of generated images. We evaluated the proposedWAEs using the Fr´echet Inception Distance (FID), In-ception Score (IS) and Structural Similarity Index Mea-sure (SSIM). The experimental results in the CelebAdataset demonstrate that WAEs significantly outperformVAEs by 25% in FID, 13.6% in IS and 15.3% in SSIM.Additionally, the evaluation considers the issue of classimbalance in the ODIR dataset, where WAEs demon-strate superior accuracy and precision in classificationtasks. Our findings highlight WAEs as a practical andefficient alternative to VAEs for image generation andreconstruction, particularly in resource-limited settings