.. _sphx_glr_auto_examples_backends:



POT backend examples
--------------------


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    <div class="sphx-glr-thumbnails">


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example we estimate mixing parameters from distributions that minimize the Wasserstein ...">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_unmix_optim_torch_thumb.png
    :alt: Wasserstein unmixing with PyTorch

  :ref:`sphx_glr_auto_examples_backends_plot_unmix_optim_torch.py`

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      <div class="sphx-glr-thumbnail-title">Wasserstein unmixing with PyTorch</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Dual OT solvers for entropic and quadratic regularized OT with Pytorch">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_dual_ot_pytorch_thumb.png
    :alt: Dual OT solvers for entropic and quadratic regularized OT with Pytorch

  :ref:`sphx_glr_auto_examples_backends_plot_dual_ot_pytorch.py`

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      <div class="sphx-glr-thumbnail-title">Dual OT solvers for entropic and quadratic regularized OT with Pytorch</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this small example, we consider the following minimization problem:">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_wass1d_torch_thumb.png
    :alt: Wasserstein 1D (flow and barycenter) with PyTorch

  :ref:`sphx_glr_auto_examples_backends_plot_wass1d_torch.py`

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      <div class="sphx-glr-thumbnail-title">Wasserstein 1D (flow and barycenter) with PyTorch</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example we train a Wasserstein GAN using Wasserstein 2 on minibatches as a distribution...">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_wass2_gan_torch_thumb.png
    :alt: Wasserstein 2 Minibatch GAN with PyTorch

  :ref:`sphx_glr_auto_examples_backends_plot_wass2_gan_torch.py`

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      <div class="sphx-glr-thumbnail-title">Wasserstein 2 Minibatch GAN with PyTorch</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this exemple we use the pytorch backend to optimize the sliced Wasserstein loss between two ...">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_sliced_wass_grad_flow_pytorch_thumb.png
    :alt: Sliced Wasserstein barycenter and gradient flow with PyTorch

  :ref:`sphx_glr_auto_examples_backends_plot_sliced_wass_grad_flow_pytorch.py`

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      <div class="sphx-glr-thumbnail-title">Sliced Wasserstein barycenter and gradient flow with PyTorch</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Continuous OT plan estimation with Pytorch">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_stoch_continuous_ot_pytorch_thumb.png
    :alt: Continuous OT plan estimation with Pytorch

  :ref:`sphx_glr_auto_examples_backends_plot_stoch_continuous_ot_pytorch.py`

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      <div class="sphx-glr-thumbnail-title">Continuous OT plan estimation with Pytorch</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we use the pytorch backend to optimize the Gromov-Wasserstein (GW) loss betwee...">

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  .. image:: /auto_examples/backends/images/thumb/sphx_glr_plot_optim_gromov_pytorch_thumb.png
    :alt: Optimizing the Gromov-Wasserstein distance with PyTorch

  :ref:`sphx_glr_auto_examples_backends_plot_optim_gromov_pytorch.py`

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      <div class="sphx-glr-thumbnail-title">Optimizing the Gromov-Wasserstein distance with PyTorch</div>
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    </div>


.. toctree::
   :hidden:

   /auto_examples/backends/plot_unmix_optim_torch
   /auto_examples/backends/plot_dual_ot_pytorch
   /auto_examples/backends/plot_wass1d_torch
   /auto_examples/backends/plot_wass2_gan_torch
   /auto_examples/backends/plot_sliced_wass_grad_flow_pytorch
   /auto_examples/backends/plot_stoch_continuous_ot_pytorch
   /auto_examples/backends/plot_optim_gromov_pytorch