Learning Disentangled Representations Using Dormant Variations

A disentangled representation is one in which each variable in the latent space is sensitive to one single generative factor and is relatively dormant to other factors. Disentanglement results in an incisive latent representation of the image which can be used for downstream tasks such as reinforcement learning and supervised learning. The discrete generative factors in image datasets are hard to capture in the form of a latent space and in order to perform efficient interpolations it requires smooth and continuous latent spaces in order to address this by disentangling the important factors of the input image in the latent space. Subsequently post training the model should be able to generate different versions of the input image by varying features/attributes. A technique Hybrid Optimized GAN using Dormant Variants (HOGDV) is proposed which can be deployed in multiple places if the number is made variable and works on a wide variety of data distribution.