Drawing out of Distribution with Neuro-Symbolic Generative Models
Authors: Yichao Liang, Josh Tenenbaum, Tuan Anh Le, Siddharth N
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Doo D on its ability to generalise across both data and tasks. We first perform zero-shot transfer from one dataset (e.g. MNIST) to another (e.g. Quickdraw), across five different datasets, and show that Doo D clearly outperforms different baselines. An analysis of the learnt representations further highlights the benefits of adopting a symbolic stroke model. We then adopt a subset of the Omniglot challenge tasks, and evaluate its ability to generate new exemplars (both unconditionally and conditionally), and perform one-shot classification, showing that Doo D matches the state of the art. |
| Researcher Affiliation | Collaboration | Yichao Liang1,2, Joshua B. Tenenbaum2, Tuan Anh Le ,3 & N. Siddharth ,4 1University of Oxford, 2MIT, 3Google, 4University of Edinburgh |
| Pseudocode | No | The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor are there structured algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We train Doo D and Attend-Infer-Repeat (AIR) [9]2, an unsupervised part-based model, on each of five stroke-based image datasets (i) MNIST (handwritten digits) [26], (ii) EMNIST (handwritten digits and letters) [7], (iii) KMNIST (cursive Japanese characters) [6], (iv) Quickdraw (doodles) [15], and (v) Omniglot (handwritten characters from multiple alphabets) [24] |
| Dataset Splits | No | We train on MNIST and show sample reconstructions from all five datasets without fine tuning. Models are trained on each source dataset and tested on each target dataset, resulting in a 5 × 5 table for each model (Fig. 4). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and libraries like Spatial Transformers, RNNs, GMM, Bernoulli, Laplace, NVIL, ADAM, and PyTorch, but does not specify their version numbers. |
| Experiment Setup | Yes | Note that we limit the maximum number of strokes to 6 throughout all experiments. estimated using the importance weighted autoencoder (IWAE) objective [3] with 200 samples (mean and standard deviation over five runs). Furthermore, in order to ensure that the ELBO objective is appropriately balanced, we employ additional weighting β for the KL-divergence over stopping criterion ot within the objective [2, 19] (see Appendix C.1 for details). |