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 [1].
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. |