SketchODE: Learning neural sketch representation in continuous time
Authors: Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our models are validated on several datasets including Vector MNIST, Di Di and Quick, Draw!. |
| Researcher Affiliation | Collaboration | Ayan Das1,2, Yongxin Yang1,3, Timothy Hospedales1,3, Tao Xiang1,2 & Yi-Zhe Song1,2 1Sketch X, CVSSP, University of Surrey, UK 2i Fly Tek-Surrey Joint Research Centre on Artificial Intelligence 3School of Informatics, University of Edinburgh, UK |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the methodology described. |
| Open Datasets | Yes | Quick Draw:1 Released by Ha & Eck (2018), Quick, Draw! is the largest collection of publicly available free-hand doodling dataset. ... 1https://github.com/googlecreativelab/quickdraw-dataset. Di Di Dataset2: Released by Gervais et al. (2020), Digital Ink Diagram dataset is a large collection of synthetically generated flowcharts ... 2https://github.com/google-research/google-research/tree/master/didi dataset |
| Dataset Splits | No | We use a 80% 20% train-test split for each. While the paper shows "Validation error" in Figure 5, it does not explicitly provide details about a specific validation dataset split (e.g., as a percentage or count) distinct from the training and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Adam W (Loshchilov & Hutter, 2019) optimizer" and "RK4 (The classic Runge-Kutta method)" but does not specify software versions for any libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | The dimensionality of the hidden state in CDE/ODE is denoted d and the latent dimension as l. For experimentation, we choose d = 64, l = 48 for Vector MNIST, d = 96, l = 84 for Di Di and d = 144, l = 96 for Quick, Draw!. Dynamics networks (for both CDE and ODE) are MLPs with few (2 for full-sequence, 1 for multi-stroke) hidden layers of size 1.2d . ... We empirically found the best value of the upper limit of time range to be T = 5. All models are trained with Adam W (Loshchilov & Hutter, 2019) optimizer, learning rate of 1 × 10−4 for RNN and 3 × 10−3 for CDE-ODE, annealed using cosine scheduler (100 epoch period) with decreasing (by factor of 4) amplitude. We also used 10−2 as weight decay (L2 penalty) regularizer on the weight of the dynamics networks and gradients are clipped at a magnitude of 0.01. |