MULTI ATTRIBUTE CONTROLLABLE IMAGE SYNTHESIS DECOMPOSED GAN

Mrs.Sowjanya; K.Manasa; M.Soujanya; G.Shashank

MULTI ATTRIBUTE CONTROLLABLE IMAGE SYNTHESIS DECOMPOSED GAN

Authors: Mrs.Sowjanya, K.Manasa, M.Soujanya, G.Shashank

Unique Paper ID: 177802
Volume: 11
Issue: 12
PageNo: 3386-3389

Keywords: No Keywords Found

Abstract:
This project presents a novel approach to controllable image synthesis through the implementation of Attribute-Decomposed Generative Adversarial Networks (ADGAN) and its enhanced version, ADGAN++. Unlike traditional GAN-based models that often suffer from entangled attribute representations and lack of fine-grained control, the proposed system effectively disentangles individual facial attributes into independent latent spaces. This enables precise and isolated modifications of attributes such as age, gender, expression, and hairstyle without affecting other image features. ADGAN++ further improves attribute manipulation by employing a serial encoding strategy, allowing for smoother and more accurate transitions during image editing. The system incorporates a structured pipeline involving data preprocessing, attribute decomposition, and high-quality image generation using state-of-the-art GAN architectures. With applications ranging from facial editing and personalization to data augmentation and medical imaging, this work demonstrates significant advancements in the usability and interpretability of GAN-based image synthesis techniques.

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ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 3386-3389

MULTI ATTRIBUTE CONTROLLABLE IMAGE SYNTHESIS DECOMPOSED GAN

Available:https://ijirt.org/Article?manuscript=177802

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