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..
ChiroDiff: Modelling chirographic data with Diffusion Models
Authors: Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform quantitative and qualitative evaluation of our framework on relevant datasets and found it to be better or on par with competing approaches. |
| Researcher Affiliation | Collaboration | Ayan Das1,2, Yongxin Yang1,3, Timothy Hospedales1,4,5, Tao Xiang1,2 & Yi-Zhe Song1,2 1Sketch X, CVSSP, University of Surrey; 2i Fly Tek-Surrey Joint Research Centre on AI; 3Queen Mary University of London; 4University of Edinburgh, 5Samsung AI Centre, Cambridge |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Please refer to the project page3 for full source code. 3Our project page: https://ayandas.me/chirodiff |
| Open Datasets | Yes | Vector MNIST or VMNIST (Das et al., 2022), Kanji VG1 is a vector dataset containing Kanji characters. We use a preprocessed version of the dataset2 which converted the original SVGs into polyline sequences. 1Original Kanji VG: kanjivg.tagaini.net 2Pre-processed Kanji VG: github.com/hardmaru/sketch-rnn-datasets/tree/master/kanji |
| Dataset Splits | Yes | We use 80-10-10 splits for our all our experimentation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using ‘Adam W optimizer’ but does not specify other key software components (e.g., deep learning framework, programming language) with version numbers. |
| Experiment Setup | Yes | CHIRODIFF s forward process, just like traditional DDPMs, uses a linear noising schedule of βmin = 10 4 1000/T, βmax = 2 10 2 1000/T... we choose a standard value of T = 1000. ... We use a 2-layer GRU with D = 48 hidden units for VMNIST and 3-layer GRU for Quick Draw (D = 128) and Kanji VG (D = 96). We trained all of our models by minimizing Eq. 3 using Adam W optimizer... and step-wise LR scheduling of γe = 0.9997 γe 1 at every epoch e where γ0 = 6 10 3. ...We found σ2 t = 0.8 βt to work well in majority of the cases... |