Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
Authors: Jian Chen, Ruiyi Zhang, Yufan Zhou, Changyou Chen
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results show that LACE produces high-quality layouts and outperforms existing state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | University at Buffalo1, Adobe Research2, MBZUAI3 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code is available at https://github.com/puar-playground/LACE |
| Open Datasets | Yes | We use two large-scale datasets for comparison. Pub Lay Net (Zhong et al., 2019) consists of 330K document layouts... Rico (Deka et al., 2017) consists of 72k user interfaces designs... |
| Dataset Splits | Yes | The refined Pub Lay Net and Rico datasets were then split into training, validation, and test sets containing 315, 757/16, 619/11, 142 and 35, 851/2, 109/4, 218 samples respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The batch size is 256. We used a learning rate schedule that included a warmup phase followed by a half-cycle cosine decay. The initial learning rate is set to 0.001. ... The diffusion model employs a total of 1000 forward steps. For efficient generation, we use DDIM sampling with 100 steps. |