Ivan Ustyuzhaninov, Matthias Bethge, David Klindt, Lukas Schott, Yash Sharma, Wieland Brendel, Dylan Paiton
We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bethgelab/slow_disentanglement | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_data | Ran | bethgelab/slow_disentanglement/data_generation/gen_kitti_masks.py code served (permissive licence) · get_code("e6b9f4019999542d") |
| get_individual_sequences | Ran | bethgelab/slow_disentanglement/data_generation/gen_kitti_masks.py code served (permissive licence) · get_code("9fb34afbc3ab0caa") |
| get_latents | Ran | bethgelab/slow_disentanglement/data_generation/gen_kitti_masks.py code served (permissive licence) · get_code("87358f19b244adc7") |
| make_cost_matrix | Ran | bethgelab/slow_disentanglement/mcc_metric/munkres.py code served (permissive licence) · get_code("60f32cea809132df") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Disentangling the underlying generative factors from data has so far been limited to carefully constructed scenarios. We propose a path towards natural data by first showing that the statistics of natural data provide enough structure to enable disentanglement, both theoretically and empirically. Specifically, we provide evidence that objects in natural movies undergo transitions that are typically small in magnitude with occasional large jumps, which is characteristic of a temporally sparse distribution. Leveraging this finding we provide a novel proof that relies on a sparse prior on temporally adjacent observations to recover the true latent variables up to permutations and sign flips, providing a stronger result than previous work. We show that equipping practical estimation methods with our prior often surpasses the current state-of-the-art on several established benchmark datasets without any impractical assumptions, such as knowledge of the number of changing generative factors. Furthermore, we contribute two new benchmarks, Natural Sprites and KITTI Masks, which integrate the measured natural dynamics to enable disentanglement evaluation with more realistic datasets. We test our theory on these benchmarks and demonstrate improved performance. We also identify non-obvious challenges for current methods in scaling to more natural domains. Taken together our work addresses key issues in disentanglement research for moving towards more natural settings.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.10930")
get_code_for_paper("2007.10930")
have("2007.10930")
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