Brandon Amos
We lifted 1 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| facebookresearch/w2ot | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| plot_conj | Not yet run | facebookresearch/w2ot/scripts/vis-2d-transport.py code served (permissive licence) · get_code("7fb9bfa9b31ec695") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper focuses on computing the convex conjugate (also known as the Legendre-Fenchel conjugate or c-transform) that appears in Euclidean Wasserstein-2 optimal transport. This conjugation is considered difficult to compute and in practice, methods are limited by not being able to exactly conjugate the dual potentials in continuous space. To overcome this, the computation of the conjugate can be approximated with amortized optimization, which learns a model to predict the conjugate. I show that combining amortized approximations to the conjugate with a solver for fine-tuning significantly improves the quality of transport maps learned for the Wasserstein-2 benchmark by Korotin et al. (2021a) and is able to model many 2-dimensional couplings and flows considered in the literature. All baselines, methods, and solvers are publicly available at http://github.com/facebookresearch/w2ot. 'log_alpha', nn.initializers.constant(0), []) 'log_alpha', nn.initializers.constant(0), [])
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.12153")
get_code_for_paper("2210.12153")
have("2210.12153")
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