AI RESEARCH
A Unified Latent Space Disentanglement VAE Framework with Robust Disentanglement Effectiveness Evaluation
arXiv CS.LG
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ArXi:2603.11242v1 Announce Type: cross Evaluating and interpreting latent representations, such as variational autoencoders (VAEs), remains a significant challenge for diverse data types, especially when ground-truth generative factors are unknown. To address this, we propose a general framework -- bfVAE -- that unifies several state-of-the-art disentangled VAE approaches and generates effective latent space disentanglement, especially for tabular data.