| dc.description.abstract | Variational autoencoders (VAEs) are widely used for generative modeling and repre-
sentation learning tasks. This thesis presents two novel approaches aimed at enhancing
the performance of VAEs through the integration of semi-conditional variational autoen-
coders (SCVAEs). The integration of SCVAEs and VAEs is motivated by the potential
for improvement in the effectiveness of capturing the underlying data distribution and
the improvement in generating high-quality samples.
The first method extends the traditional VAEs by incorporating a second conditioned
decoder, thereby enabling the model to multi-task and learn better latent representations.
The second method utilizes a unified decoder for both tasks by employing sophisticated
training strategies. These approaches are implemented and evaluated on Gaussian VAEs
and VQ-VAEs.
Extensive experimentation is conducted across diverse image datasets, including
MNIST, CIFAR10, and CelebA. The results show that, in certain cases, the proposed
methods yield superior performance compared to standard VAE architectures. By bridg-
ing the gap between SCVAEs and VAEs, this work gives new insights on how to improve
the methods further and opens up new avenues for future research in the field of generative
modeling. | |
