Group Equivariant Subsampling
Authors: Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Teh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the performance of GAEs with equivariant subsampling to their nonequivariant counterparts that use standard subsampling/upsampling in object-centric representation learning. We show that GAEs give rise to more interpretable representations that show better sample complexity and generalisation than their non-equivariant counterparts. In Appendix E.1, we show that we can also observe generalisation performance gains when using group equivariant subsampling for classification tasks. |
| Researcher Affiliation | Collaboration | 1 Department of Statistics, University of Oxford, UK. 2 Deep Mind, UK. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It provides mathematical definitions of operations. |
| Open Source Code | Yes | Our implementation is built upon open source projects Harris et al. (2020); Paszke et al. (2019); Yadan (2019); Weiler and Cesa (2019b); Engelcke et al. (2020); Hunter (2007); Waskom (2021). 3https://github.com/jinxu06/gsubsampling |
| Open Datasets | Yes | To demonstrate basic properties of GAEs and compare sample complexity under the single object scenario, we use Colored-d Sprite (Matthey et al., 2017) and a modification of Fashion MNIST (Xiao et al., 2017), where we first apply zero-padding to reach a size of 64 × 64, followed by random shifts, rotations and coloring. For multi-object datasets, we use Multi-d Sprites (Kabra et al., 2019) and CLEVR6 which is a variant of CLEVR (Johnson et al., 2017) with up to 6 objects. |
| Dataset Splits | No | The paper mentions training and testing data, but does not provide specific information about validation dataset splits (e.g., percentages or counts for a separate validation set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | Our implementation is built upon open source projects Harris et al. (2020); Paszke et al. (2019); Yadan (2019); Weiler and Cesa (2019b); Engelcke et al. (2020); Hunter (2007); Waskom (2021). The paper lists software projects but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | See Appendix F and our reference implementation for more details on hyperparameters and data preprocessing. |