Enhancing Image Customization through Attribute Decomposed Gan for Precise Control

Kandhi Vaman Reddy, Srilatha Puli

Abstract: Controllable Image Synthesis With Attribute - Decomposed GAN (AD - GAN) proposes a novel generative adversarial network framework that facilitates precise control over image synthesis by decomposing attributes into separate components. This model introduces an innovative method of disentangling image attributes, allowing for individual modification of specific features without affecting others. By utilizing an attribute - decomposed representation, AD - GAN effectively isolates various elements, such as pose, expression, and identity in facial images, enabling the generation of highly realistic and customizable images. This approach significantly enhances the flexibility and accuracy of image generation tasks, making it a valuable tool for applications requiring detailed attribute manipulation.

Keywords: RNN, GAN, AD - GAN, image synthesis, Facial Images, Generative adversarial