Provably Learning Object-Centric Representations
Authors: Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius Von Kügelgen, Wieland Brendel
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our results through experiments on synthetic data. Finally, we provide evidence that our theory holds predictive power for existing object-centric models by showing a close correspondence between models compositionality and invertibility and their empirical identifiability. |
| Researcher Affiliation | Academia | 1MPI for Intelligent Systems, T ubingen 2T ubingen AI Center, T ubingen 3University of T ubingen, T ubingen, Germany 4Department of Engineering, University of Cambridge, Cambridge, United Kingdom. |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code/Website: brendel-group.github.io/objects-identifiability |
| Open Datasets | Yes | We generate image data using the Spriteworld renderer (Watters et al., 2019). |
| Dataset Splits | Yes | We train on 75,000 samples and use 6,000 and 5,000 for validation and test sets, respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, or TPU versions) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "PyTorch (Paszke et al., 2019)" but does not specify a precise version number for this or any other software dependency, which is required for reproducibility. |
| Experiment Setup | Yes | We train for 100 epochs with the Adam optimizer (Kingma & Ba, 2015) on batches of 64 with an initial learning rate of 10 3, which we decay by factor of 10 after 50 epochs. |