Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Provably Learning Object-Centric Representations
Authors: Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius Von Kügelgen, Wieland Brendel
ICML 2023 | Venue PDF | 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. |