MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action
Authors: Geonho Hwang, Jaewoong Choi, Hyunsoo Cho, Myungjoo Kang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the combinatorial generalizability, we evaluated our model in two settings: Recombination-to-Element and Recombination-to-Range. The experiments demonstrated that our method has quantitatively and qualitatively superior generalizability and generates better images than traditional models. |
| Researcher Affiliation | Academia | 1Korea Institute for Advanced Study, Seoul, South Korea 2Seoul National University, Seoul, South Korea. |
| Pseudocode | Yes | Detailed architectures are in Appendix A. Table 4. The Encoder Architecture. Table 5. The Flow Step Architecture. Table 6. The Decoder Architecture. Table 7. The Flow Net Architecture. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | Yes | We evaluate our model on two datasets, the d Sprites dataset (Matthey et al., 2017) and the 3D Shapes dataset (Burgess & Kim, 2018). |
| Dataset Splits | Yes | To test the combinatorial generalization property of the model, we measure the reconstruction error while separating the training and test data. Following the evaluation protocol of Montero et al. (2020), we evaluate the combinatorial generalization under two settings, the Recombination-to Element and the Recombination-to-Range. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances with their specifications). |
| Software Dependencies | No | The paper mentions 'The optimizer is Adam' but does not provide specific version numbers for Adam or any other software libraries or dependencies used. |
| Experiment Setup | Yes | The optimizer is Adam, with a learning rate of 0.0005. The dimension of the latent variable is set to 10 and the batch size is 64. For the regularizing coefficients β, we set the value to βrecon latent = 300, and βKL = 300. We trained 100 epochs for both datasets three times and took the model with the best binary cross entropy loss model. |