ChiroDiff: Modelling chirographic data with Diffusion Models
Authors: Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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... |