The Curse of Conditions: Analyzing and Improving Optimal Transport for
  Conditional Flow-Based Generation
Minibatch optimal transport coupling straightens paths in unconditional flow
matching. This leads to computationally less demanding inference as fewer
integration steps and less complex numerical solvers can be employed when
numerically solving an ordinary differential equation at test time. However, in
the conditional setting, minibatch optimal transport falls short. This is
because the default optimal transport mapping disregards conditions, resulting
in a conditionally skewed prior distribution during training. In contrast, at
test time, we have no access to the skewed prior, and instead sample from the
full, unbiased prior distribution. This gap between training and testing leads
to a subpar performance. To bridge this gap, we propose conditional optimal
transport C^2OT that adds a conditional weighting term in the cost matrix when
computing the optimal transport assignment. Experiments demonstrate that this
simple fix works with both discrete and continuous conditions in
8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method
performs better overall compared to the existing baselines across different
function evaluation budgets. Code is available at
https://hkchengrex.github.io/C2OT
67d39ae1faaad4ed2df1cc61
Ho Kei Cheng
claimed_verified
2025-03-14T08:56:19.361Z
63041b541dd5d3c62486c294
/avatars/a5286d562f7b9082730f760e66c3bf29.svg
Ho Kei Cheng
user
hkchengrex
67d39ae1faaad4ed2df1cc62
Alexander Schwing
The Curse of Conditions: Analyzing and Improving Optimal Transport for
  Conditional Flow-Based Generation
Minibatch optimal transport coupling straightens paths in unconditional flow
matching. This leads to computationally less demanding inference as fewer
integration steps and less complex numerical solvers can be employed when
numerically solving an ordinary differential equation at test time. However, in
the conditional setting, minibatch optimal transport falls short. This is
because the default optimal transport mapping disregards conditions, resulting
in a conditionally skewed prior distribution during training. In contrast, at
test time, we have no access to the skewed prior, and instead sample from the
full, unbiased prior distribution. This gap between training and testing leads
to a subpar performance. To bridge this gap, we propose conditional optimal
transport C^2OT that adds a conditional weighting term in the cost matrix when
computing the optimal transport assignment. Experiments demonstrate that this
simple fix works with both discrete and continuous conditions in
8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method
performs better overall compared to the existing baselines across different
function evaluation budgets. Code is available at
https://hkchengrex.github.io/C2OT
Conditional optimal transport C^2OT improves upon minibatch optimal transport by incorporating conditional weighting, enhancing performance in both discrete and continuous settings.
minibatch optimal transport optimal transport coupling flow matching ordinary differential equation conditional optimal transport C^2OT prior distribution 8gaussians-to-moons CIFAR-10 ImageNet-32x32 ImageNet-256x256