Bouguila, M. Khaled (2024) Conditional Generative Modeling for Layout-Transforming PFE - Project Graduation, ENSTA.

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Abstract

This report presents the work conducted during a research internship at Criteo, focusing on Conditional Generative Modeling for Layout Transformation, a novel task in the field of image-to-image translation. Traditional models, such as Cycle￾GAN, often struggle with significant layout differences between input and output images, as they tend to converge on mappings that retain similar overall struc￾tures. This work investigates methods to overcome this challenge by employing state-of-the-art generative models, namely Diffusion Models and MaskGIT. To tackle layout transformation, the study introduces novel approaches includ￾ing the creation of a synthetic dataset and a new evaluation metric tailored to this task. Through extensive experimentation and architectural improvements, the re￾sults demonstrate both the strengths and limitations of current models, proposing new directions for latent space design to better handle diverse layouts. This work lays the foundation for future research in improving generative models for complex layout transformations.

Item Type:Thesis (PFE - Project Graduation)
Uncontrolled Keywords:Conditional Generative Modeling, Layout Transforma￾tion, Image-to-Image Translation, Diffusion Models, MaskGIT, Generative AI, Latent Space Design
Subjects:Mathematics and Applications
ID Code:10432
Deposited By:Khaled BOUGUILA
Deposited On:11 oct. 2024 19:50
Dernière modification:11 oct. 2024 19:50

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