Peter Sorrenson, Ullrich Köthe, Felix Draxler, Armand Rousselot, Sander Hummerich
We lifted 7 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| vislearn/FFF | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| build_model | Ran | vislearn/FFF/fff/base.py code served (permissive licence) · get_code("615bdd56e7131c04") |
| get_module | Ran | vislearn/FFF/fff/model/utils.py code served (permissive licence) · get_code("62beb8156e773f1e") |
| guess_image_shape | Ran | vislearn/FFF/fff/model/utils.py code served (permissive licence) · get_code("b5063fada3aaa1f8") |
| make_dense | Ran | vislearn/FFF/fff/model/utils.py code served (permissive licence) · get_code("505ce06e6c35e263") |
| rand_log_uniform | Ran | vislearn/FFF/fff/base.py code served (permissive licence) · get_code("08ea55cee9bcbaa8") |
| soft_heaviside | Ran | vislearn/FFF/fff/base.py code served (permissive licence) · get_code("dc02abdd7ec5f6ca") |
| make_res_net | Not yet run | vislearn/FFF/fff/model/res_net.py code served (permissive licence) · get_code("bec3f11465bbb13c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose Manifold Free-Form Flows (M-FFF), a simple new generative model for data on manifolds. The existing approaches to learning a distribution on arbitrary manifolds are expensive at inference time, since sampling requires solving a differential equation. Our method overcomes this limitation by sampling in a single function evaluation. The key innovation is to optimize a neural network via maximum likelihood on the manifold, possible by adapting the free-form flow framework to Riemannian manifolds. M-FFF is straightforwardly adapted to any manifold with a known projection. It consistently matches or outperforms previous single-step methods specialized to specific manifolds. It is typically two orders of magnitude faster than multi-step methods based on diffusion or flow matching, achieving better likelihoods in several experiments. We provide our code at https://github.com/vislearn/FFF.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.09852")
get_code_for_paper("2312.09852")
have("2312.09852")
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