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Arxiv
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L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation
- Researchers have introduced a new model called Learnable VAE (L-VAE) that learns a disentangled representation along with cost function hyperparameters.
- L-VAE extends eta-VAE by dynamically adjusting the hyperparameter eta and learning the weights of loss function terms for better control over disentanglement and reconstruction losses.
- The L-VAE model simultaneously learns the weights of loss terms and model parameters, with an added regularization term to avoid bias towards either reconstruction or disentanglement losses.
- Experimental results demonstrate that L-VAE achieves a good balance between reconstruction accuracy and disentangled latent dimensions, outperforming or matching other VAE variants on various datasets.
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