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diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
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+# -*- coding: utf-8 -*-
+"""
+=====================================================
+OT for image color adaptation with mapping estimation
+=====================================================
+
+OT for domain adaptation with image color adaptation [6] with mapping
+estimation [8].
+
+[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
+discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+
+[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
+discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
+
+"""
+
+# Authors: Remi Flamary <remi.flamary@unice.fr>
+#          Stanislas Chambon <stan.chambon@gmail.com>
+#
+# License: MIT License
+
+# sphinx_gallery_thumbnail_number = 3
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+
+r = np.random.RandomState(42)
+
+
+def im2mat(I):
+    """Converts and image to matrix (one pixel per line)"""
+    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
+
+
+def mat2im(X, shape):
+    """Converts back a matrix to an image"""
+    return X.reshape(shape)
+
+
+def minmax(I):
+    return np.clip(I, 0, 1)
+
+
+##############################################################################
+# Generate data
+# -------------
+
+# Loading images
+I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256
+
+
+X1 = im2mat(I1)
+X2 = im2mat(I2)
+
+# training samples
+nb = 1000
+idx1 = r.randint(X1.shape[0], size=(nb,))
+idx2 = r.randint(X2.shape[0], size=(nb,))
+
+Xs = X1[idx1, :]
+Xt = X2[idx2, :]
+
+
+##############################################################################
+# Domain adaptation for pixel distribution transfer
+# -------------------------------------------------
+
+# EMDTransport
+ot_emd = ot.da.EMDTransport()
+ot_emd.fit(Xs=Xs, Xt=Xt)
+transp_Xs_emd = ot_emd.transform(Xs=X1)
+Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
+
+# SinkhornTransport
+ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
+ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
+transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
+Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
+
+ot_mapping_linear = ot.da.MappingTransport(
+    mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
+ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
+
+X1tl = ot_mapping_linear.transform(Xs=X1)
+Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
+
+ot_mapping_gaussian = ot.da.MappingTransport(
+    mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
+ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
+
+X1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping
+Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
+
+
+##############################################################################
+# Plot original images
+# --------------------
+
+pl.figure(1, figsize=(6.4, 3))
+pl.subplot(1, 2, 1)
+pl.imshow(I1)
+pl.axis('off')
+pl.title('Image 1')
+
+pl.subplot(1, 2, 2)
+pl.imshow(I2)
+pl.axis('off')
+pl.title('Image 2')
+pl.tight_layout()
+
+
+##############################################################################
+# Plot pixel values distribution
+# ------------------------------
+
+pl.figure(2, figsize=(6.4, 5))
+
+pl.subplot(1, 2, 1)
+pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
+pl.axis([0, 1, 0, 1])
+pl.xlabel('Red')
+pl.ylabel('Blue')
+pl.title('Image 1')
+
+pl.subplot(1, 2, 2)
+pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
+pl.axis([0, 1, 0, 1])
+pl.xlabel('Red')
+pl.ylabel('Blue')
+pl.title('Image 2')
+pl.tight_layout()
+
+
+##############################################################################
+# Plot transformed images
+# -----------------------
+
+pl.figure(2, figsize=(10, 5))
+
+pl.subplot(2, 3, 1)
+pl.imshow(I1)
+pl.axis('off')
+pl.title('Im. 1')
+
+pl.subplot(2, 3, 4)
+pl.imshow(I2)
+pl.axis('off')
+pl.title('Im. 2')
+
+pl.subplot(2, 3, 2)
+pl.imshow(Image_emd)
+pl.axis('off')
+pl.title('EmdTransport')
+
+pl.subplot(2, 3, 5)
+pl.imshow(Image_sinkhorn)
+pl.axis('off')
+pl.title('SinkhornTransport')
+
+pl.subplot(2, 3, 3)
+pl.imshow(Image_mapping_linear)
+pl.axis('off')
+pl.title('MappingTransport (linear)')
+
+pl.subplot(2, 3, 6)
+pl.imshow(Image_mapping_gaussian)
+pl.axis('off')
+pl.title('MappingTransport (gaussian)')
+pl.tight_layout()
+
+pl.show()