Disentangled Representation Learning Using ($\beta$-)VAE and GAN

• URL: http://arxiv.org/abs/2208.04549v1
• Date: Tue, 9 Aug 2022 05:37:06 GMT
• Title: Disentangled Representation Learning Using ($\beta$-)VAE and GAN
• Authors: Mohammad Haghir Ebrahimabadi
• Abstract summary: The dSprite dataset provided the desired features for the required experiments. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space vector of the VAE was the task of interest in this paper. The dSprite dataset provided the desired features for the required experiments in this research. After training the VAE combined with a Generative Adversarial Network (GAN), each dimension of the hidden vector was disrupted to explore the disentanglement in each dimension. Note that the GAN was used to improve the quality of output image reconstruction.

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