A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation
| dc.contributor.advisor | Beakal Gizachew. (PhD) | |
| dc.contributor.author | Azmeraw Bekele | |
| dc.date.accessioned | 2024-10-28T08:27:07Z | |
| dc.date.available | 2024-10-28T08:27:07Z | |
| dc.date.issued | 2024-05 | |
| dc.description.abstract | Generative models have revolutionized various industries by enabling the generation of diverse outputs, and floorplan generation is one such application. Different methods, including GANs, diffusion models, and others, have been employed for floorplan generation. However, each method faces specific challenges, such as mode collapse in GANs and sampling time in diffusion models. Efforts to mitigate these issues have led to the exploration of techniques such as regularization methods, architectural modifications, knowledge distillation, and adaptive noise schedules. However, existing methods often struggle to effectively balance both sampling time and diversity simultaneously. In response, this thesis proposes a novel hybrid approach that amalgamates GANs and diffusion models to address the dual challenges of diversity and sampling time in floorplan generation. To the best of our knowledge, this work is the first to introduce a solution that not only balances sampling time and diversity but also enhances the realism of the generated floorplans. The proposed method is trained on the RPLAN dataset and combines the advantages of GANs and diffusion models while incorporating different techniques such as regularization methods and architectural modifications to optimize our objectives. To evaluate the effect of the denoising step, we experimented with different time steps and found better diversity results at T=20. The diversity of generated floorplans was evaluated using FID across the number of rooms, and the results of our model demonstrate an average 15.5% improvement over the state-of-the-art houseDiffusion model. Additionally, it reduces the time required for generation by 41% compared to the housediffusion model. Despite these advancements, it is acknowledged that the proposed research may encounter limitations in generating non-Manhattan floorplans and when dealing with complex layouts. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3514 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Diffusion model | |
| dc.subject | Diversity | |
| dc.subject | Floorplan | |
| dc.subject | GAN | |
| dc.subject | sampling time | |
| dc.subject | mode collapse | |
| dc.title | A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation | |
| dc.type | Thesis |