SCAN: Learning Hierarchical Compositional Visual Concepts
Authors: Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bošnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of SCAN on a dataset of visual frames and corresponding symbolic descriptions collected within the Deep Mind Lab environment (Beattie et al., 2016). |
| Researcher Affiliation | Industry | Deep Mind, London, UK {irinah,sonnerat,lmatthey,arkap,cpburgess, matko,botvinick,demishassabis,lerchner}@google.com |
| Pseudocode | No | The paper contains architectural diagrams (e.g., Figure 2A, Figure 3A) but no formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | Environment We evaluate the performance of SCAN on a dataset of visual frames and corresponding symbolic descriptions collected within the Deep Mind Lab environment (Beattie et al., 2016). We ran additional experiments on a more realistic dataset Celeb A (Liu et al., 2015)... In this section we describe additional experiments testing SCAN on the d Sprites (Matthey et al., 2017) dataset. |
| Dataset Splits | Yes | We split the dataset into two subsets. One was used for training the models, while the other one contained a held out set of 300 four-gram concepts that were never seen during training, either visually or symbolically. The accuracy and diversity metrics were calculated on two sets of sym2img samples: 1) train, corresponding to the 133 symbols used to train the models; and 2) test (symbols), corresponding to a held out set of 50 symbols. |
| Hardware Specification | No | The paper mentions using TensorFlow but does not provide specific details on the hardware used, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The padding algorithm used was SAME in Tensor Flow (Abadi et al., 2015). The optimiser used was Adam (Kingma & Ba, 2015) with a learning rate of 1e 3 and ϵ = 1e 8. |
| Experiment Setup | Yes | We set βy = 1 for all experiments, and λ = 10. We trained the model using Adam optimiser with learning rate of 1e 4 and batch size 16. |