"Sliced Wasserstein Distance for Learning Gaussian Mixture Models", CVPR'18 which defines the sliced-Wasserstein means problem, and describes a novel technique for fitting Gaussian Mixture Models to data. In short, the method minimizes the sliced-Wasserstein distance between the data distribution and ...
Sliced Wasserstein Distance (SWD) in PyTorch. Contribute to koshian2/swd-pytorch development by creating an account on GitHub.
In this paper, we describe a perceptual CD measure based on the multiscale sliced Wasserstein distance, which facilitates efficient comparisons between non-local patches of similar color and structure. This aligns with the modern understanding of color perception, where color and structure are ...
Several variants of Sliced Wasserstein distances (SW) are introduced to reduce the computation cost, but bring new problems to the situation: The vanilla SW treats sampled slices equally, resulting in redundant projections; Distributional Sliced Wasserstein distance requires gradient-based optimization, ...
You can train this model with 'max' and 'normal' mode, which means using the Maximum Sliced-Wasserstein distance and the normal Sliced-Wasserstein distance, respectively. To train with 'max' mode please run:python examples/mnist.py --mode 'max' --mode_test 'max'. ...
In particular the Sliced-Wasserstein distance (SW), which leverages one-dimensional projections for which a closed-form solution of the Wasserstein distance is available, has received a lot of interest. Yet, it is restricted to data living in Euclidean spaces, while the Wasserstein distance has ...
The sliced Wasserstein distance with the corresponding output object. """ if proj_fn is None: if projector is None: Contributor Author marcocuturi Sep 13, 2024 I think this is a bit complex, no? it just saves the user lambda arr, **_: arr @ projector.T when passing proj_fn as...
Sliced Iterative Generator (SIG) & Gaussianizing Iterative Slicing (GIS) - SINF/sinf/SlicedWasserstein.py at master · biweidai/SINF