TopoDiffusionNet: A Topology-aware Diffusion Model
Authors: Saumya Gupta, Dimitris Samaras, Chao Chen
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments across four datasets demonstrate significant improvements in topological accuracy. 4 EXPERIMENTS Datasets. We train ADM-T and TDN on four datasets: Shapes, COCO (Caesar et al., 2018), CREMI (Funke et al., 2016), and Google Maps (Isola et al., 2017). |
| Researcher Affiliation | Academia | Saumya Gupta, Dimitris Samaras & Chao Chen Department of Computer Science Stony Brook University Stony Brook, NY 11794, USA EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in narrative text and mathematical formulas, without explicitly formatted pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/Saumya-Gupta-26/ Topo Diffusion Net |
| Open Datasets | Yes | Datasets. We train ADM-T and TDN on four datasets: Shapes, COCO (Caesar et al., 2018), CREMI (Funke et al., 2016), and Google Maps (Isola et al., 2017). |
| Dataset Splits | No | The paper mentions generating samples for evaluation, but does not provide specific training/test/validation splits for the datasets used to train the models. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | To compute persistent homology, we use the Cubical Ripser (Kaji et al., 2020) library. The paper mentions using methods like cosine noise scheduler and DDIM sampling, but does not provide specific version numbers for these or other software libraries. |
| Experiment Setup | Yes | For every dataset, we use 256 256 as the image resolution. Our diffusion models use a cosine noise scheduler (Nichol & Dhariwal, 2021), with T = 1000 timesteps for training. During inference, however, we use only 50 steps of DDIM (Song et al., 2020a) sampling. When λ = 1e 5, TDN achieves the best performance. |