COAT: Measuring Object Compositionality in Emergent Representations
Authors: Sirui Xie, Ari S Morcos, Song-Chun Zhu, Ramakrishna Vedantam
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the popular CLEVR (Johnson et.al., 2018) domain reveal that existing disentanglement-based generative models are not as compositional as one might expect, suggesting room for further modeling improvements. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, UCLA 2Fundamental AI Research (FAIR, Meta Inc.) 3Department of Statistics, UCLA. |
| Pseudocode | No | The paper describes methods and algorithms in narrative text, such as the 'Greedy Matching Algorithm,' but does not present any content explicitly labeled as 'Pseudocode' or 'Algorithm,' nor does it include structured code blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Our experiments on the popular CLEVR (Johnson et.al., 2018) domain |
| Dataset Splits | No | The paper mentions 'IID dataset' and 'highly correlated training dataset' and refers to a 'Train Set' in Table 2, but does not provide specific details on the dataset splits (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x), which would be necessary to replicate the environment. |
| Experiment Setup | Yes | All models are trained with the default architectures and hyperparameters except that in β-TC-VAE we use latent dimension 256, and use the same encoder for Mo Co. |