diff --git a/RELEASES.md b/RELEASES.md
index ec7e5774c..fa3237158 100644
--- a/RELEASES.md
+++ b/RELEASES.md
@@ -17,6 +17,7 @@
 - Backend implementation of `ot.dist` for (PR #701)
 - Updated documentation Quickstart guide and User guide with new API (PR #726)
 - Fix jax version for auto-grad (PR #732)
+- Added to each example in the examples gallery the information about the release version in which it was introduced (PR #743)
 
 #### Closed issues
 - Fixed `ot.mapping` solvers which depended on deprecated `cvxpy` `ECOS` solver (PR #692, Issue #668)
diff --git a/examples/backends/plot_Sinkhorn_gradients.py b/examples/backends/plot_Sinkhorn_gradients.py
index 229a952b1..7ef49ce46 100644
--- a/examples/backends/plot_Sinkhorn_gradients.py
+++ b/examples/backends/plot_Sinkhorn_gradients.py
@@ -6,6 +6,9 @@
 
 This example illustrates the differences in terms of computation time between the gradient options for the Sinkhorn solver.
 
+.. note::
+    Example added in release: 0.9.6
+
 """
 
 # Author: Sonia Mazelet <sonia.mazelet@polytechnique.edu>
diff --git a/examples/backends/plot_dual_ot_pytorch.py b/examples/backends/plot_dual_ot_pytorch.py
index 8449f1f60..a97b2ddb3 100644
--- a/examples/backends/plot_dual_ot_pytorch.py
+++ b/examples/backends/plot_dual_ot_pytorch.py
@@ -4,6 +4,8 @@
 Dual OT solvers for entropic and quadratic regularized OT with Pytorch
 ======================================================================
 
+.. note::
+    Example added in release: 0.8.2.
 
 """
 
diff --git a/examples/backends/plot_optim_gromov_pytorch.py b/examples/backends/plot_optim_gromov_pytorch.py
index d01cb56c5..314e27f9e 100644
--- a/examples/backends/plot_optim_gromov_pytorch.py
+++ b/examples/backends/plot_optim_gromov_pytorch.py
@@ -3,6 +3,9 @@
 Optimizing the Gromov-Wasserstein distance with PyTorch
 =======================================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example, we use the pytorch backend to optimize the Gromov-Wasserstein
 (GW) loss between two graphs expressed as empirical distribution.
 
diff --git a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
index 33d8b92be..bb7aca999 100644
--- a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
+++ b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
@@ -3,6 +3,9 @@
 Sliced Wasserstein barycenter and gradient flow with PyTorch
 ============================================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we use the pytorch backend to optimize the sliced Wasserstein
 loss between two empirical distributions [31].
 
diff --git a/examples/backends/plot_stoch_continuous_ot_pytorch.py b/examples/backends/plot_stoch_continuous_ot_pytorch.py
index d44a86f64..20c14fb9c 100644
--- a/examples/backends/plot_stoch_continuous_ot_pytorch.py
+++ b/examples/backends/plot_stoch_continuous_ot_pytorch.py
@@ -4,6 +4,8 @@
 Continuous OT plan estimation with Pytorch
 ======================================================================
 
+.. note::
+    Example added in release: 0.8.2.
 
 """
 
diff --git a/examples/backends/plot_unmix_optim_torch.py b/examples/backends/plot_unmix_optim_torch.py
index 1f48cf214..03ee62209 100644
--- a/examples/backends/plot_unmix_optim_torch.py
+++ b/examples/backends/plot_unmix_optim_torch.py
@@ -4,6 +4,9 @@
 Wasserstein unmixing with PyTorch
 =================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we estimate mixing parameters from distributions that minimize
 the Wasserstein distance. In other words we suppose that a target
 distribution :math:`\mu^t` can be expressed as a weighted sum of source
diff --git a/examples/backends/plot_wass2_gan_torch.py b/examples/backends/plot_wass2_gan_torch.py
index 806803715..e211711c7 100644
--- a/examples/backends/plot_wass2_gan_torch.py
+++ b/examples/backends/plot_wass2_gan_torch.py
@@ -4,6 +4,9 @@
 Wasserstein 2 Minibatch GAN with PyTorch
 ========================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 In this example we train a Wasserstein GAN using Wasserstein 2 on minibatches
 as a distribution fitting term.
 
diff --git a/examples/barycenters/plot_debiased_barycenter.py b/examples/barycenters/plot_debiased_barycenter.py
index f8f271947..a1b547274 100644
--- a/examples/barycenters/plot_debiased_barycenter.py
+++ b/examples/barycenters/plot_debiased_barycenter.py
@@ -4,6 +4,9 @@
 Debiased Sinkhorn barycenter demo
 =================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrates the computation of the debiased Sinkhorn barycenter
 as proposed in [37]_.
 
diff --git a/examples/barycenters/plot_free_support_sinkhorn_barycenter.py b/examples/barycenters/plot_free_support_sinkhorn_barycenter.py
index a8aa50a95..a7bcadc76 100644
--- a/examples/barycenters/plot_free_support_sinkhorn_barycenter.py
+++ b/examples/barycenters/plot_free_support_sinkhorn_barycenter.py
@@ -4,6 +4,9 @@
 2D free support Sinkhorn barycenters of distributions
 ========================================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 Illustration of Sinkhorn barycenter calculation between empirical distributions understood as point clouds
 
 """
diff --git a/examples/barycenters/plot_gaussian_barycenter.py b/examples/barycenters/plot_gaussian_barycenter.py
index 60e08348a..9a74193f4 100644
--- a/examples/barycenters/plot_gaussian_barycenter.py
+++ b/examples/barycenters/plot_gaussian_barycenter.py
@@ -4,6 +4,9 @@
 Gaussian Bures-Wasserstein barycenters
 ========================================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 Illustration of Gaussian Bures-Wasserstein barycenters.
 
 """
diff --git a/examples/barycenters/plot_generalized_free_support_barycenter.py b/examples/barycenters/plot_generalized_free_support_barycenter.py
index 5b3572bd4..a76ca63e2 100644
--- a/examples/barycenters/plot_generalized_free_support_barycenter.py
+++ b/examples/barycenters/plot_generalized_free_support_barycenter.py
@@ -4,6 +4,9 @@
 Generalized Wasserstein Barycenter Demo
 =======================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the computation of Generalized Wasserstein Barycenter
 as proposed in [42].
 
diff --git a/examples/domain-adaptation/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py
index 29d199bd0..2f1037cef 100644
--- a/examples/domain-adaptation/plot_otda_classes.py
+++ b/examples/domain-adaptation/plot_otda_classes.py
@@ -4,6 +4,9 @@
 OT for domain adaptation
 ========================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a domain adaptation in a 2D setting and the 4 OTDA
 approaches currently supported in POT.
 
diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py
index 0a452677d..932deeaf0 100644
--- a/examples/domain-adaptation/plot_otda_color_images.py
+++ b/examples/domain-adaptation/plot_otda_color_images.py
@@ -4,6 +4,9 @@
 OT for image color adaptation
 =============================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example presents a way of transferring colors between two images
 with Optimal Transport as introduced in [6]
 
diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py
index 7b38cf3b5..39fc7d730 100644
--- a/examples/domain-adaptation/plot_otda_d2.py
+++ b/examples/domain-adaptation/plot_otda_d2.py
@@ -4,6 +4,9 @@
 OT for domain adaptation on empirical distributions
 ===================================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a domain adaptation in a 2D setting. It explicit
 the problem of domain adaptation and introduces some optimal transport
 approaches to solve it.
diff --git a/examples/domain-adaptation/plot_otda_jcpot.py b/examples/domain-adaptation/plot_otda_jcpot.py
index ddc30fb64..ccd4c64a5 100644
--- a/examples/domain-adaptation/plot_otda_jcpot.py
+++ b/examples/domain-adaptation/plot_otda_jcpot.py
@@ -4,6 +4,9 @@
 OT for multi-source target shift
 ================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example introduces a target shift problem with two 2D source and 1 target domain.
 
 """
diff --git a/examples/domain-adaptation/plot_otda_laplacian.py b/examples/domain-adaptation/plot_otda_laplacian.py
index 755cfd4be..0cab77b9e 100644
--- a/examples/domain-adaptation/plot_otda_laplacian.py
+++ b/examples/domain-adaptation/plot_otda_laplacian.py
@@ -4,6 +4,9 @@
 OT with Laplacian regularization for domain adaptation
 ======================================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example introduces a domain adaptation in a 2D setting and OTDA
 approach with Laplacian regularization.
 
diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py
index 4795072f3..9900a52f2 100644
--- a/examples/domain-adaptation/plot_otda_linear_mapping.py
+++ b/examples/domain-adaptation/plot_otda_linear_mapping.py
@@ -5,6 +5,8 @@
 Linear OT mapping estimation
 ============================
 
+.. note::
+    Example updated in release: 0.9.1.
 
 """
 
diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py
index 42a89a381..4cf52c770 100644
--- a/examples/domain-adaptation/plot_otda_mapping.py
+++ b/examples/domain-adaptation/plot_otda_mapping.py
@@ -4,6 +4,9 @@
 OT mapping estimation for domain adaptation
 ===========================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example presents how to use MappingTransport to estimate at the same
 time both the coupling transport and approximate the transport map with either
 a linear or a kernelized mapping as introduced in [8].
diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
index c52bdf121..31775539f 100644
--- a/examples/domain-adaptation/plot_otda_mapping_colors_images.py
+++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
@@ -4,6 +4,9 @@
 OT for image color adaptation with mapping estimation
 =====================================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 OT for domain adaptation with image color adaptation [6] with mapping
 estimation [8].
 
diff --git a/examples/domain-adaptation/plot_otda_semi_supervised.py b/examples/domain-adaptation/plot_otda_semi_supervised.py
index 454e67ec3..fca49bb7d 100644
--- a/examples/domain-adaptation/plot_otda_semi_supervised.py
+++ b/examples/domain-adaptation/plot_otda_semi_supervised.py
@@ -4,6 +4,9 @@
 OTDA unsupervised vs semi-supervised setting
 ============================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a semi supervised domain adaptation in a 2D setting.
 It explicit the problem of semi supervised domain adaptation and introduces
 some optimal transport approaches to solve it.
diff --git a/examples/gromov/plot_fgw_solvers.py b/examples/gromov/plot_fgw_solvers.py
index ab1ccac88..d727e09d6 100644
--- a/examples/gromov/plot_fgw_solvers.py
+++ b/examples/gromov/plot_fgw_solvers.py
@@ -4,6 +4,9 @@
 Comparison of Fused Gromov-Wasserstein solvers
 ==============================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the computation of FGW for attributed graphs
 using 4 different solvers to estimate the distance based on Conditional
 Gradient [24], Sinkhorn projections [12, 51] and alternated Bregman
diff --git a/examples/gromov/plot_gnn_TFGW.py b/examples/gromov/plot_gnn_TFGW.py
index 9ec27f47d..1639dd808 100644
--- a/examples/gromov/plot_gnn_TFGW.py
+++ b/examples/gromov/plot_gnn_TFGW.py
@@ -4,6 +4,9 @@
 Graph classification with Template Based Fused Gromov Wasserstein
 ==============================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example first illustrates how to train a graph classification gnn based on the Template Fused Gromov Wasserstein layer as proposed in [52] .
 
 [53] C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer, N. Courty (2022).Template based graph neural network with optimal transport distances. Advances in Neural Information Processing Systems, 35.
diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py
index b376af642..c2ac3e43f 100644
--- a/examples/gromov/plot_gromov.py
+++ b/examples/gromov/plot_gromov.py
@@ -3,6 +3,10 @@
 ==========================
 Gromov-Wasserstein example
 ==========================
+
+.. note::
+    Example added in release: 0.8.0.
+
 This example is designed to show how to use the Gromov-Wasserstein distance
 computation in POT.
 We first compare 3 solvers to estimate the distance based on
diff --git a/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py b/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
index 4b94c9d40..472fdf904 100755
--- a/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
+++ b/examples/gromov/plot_gromov_wasserstein_dictionary_learning.py
@@ -5,6 +5,9 @@
 (Fused) Gromov-Wasserstein Linear Dictionary Learning
 =====================================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 In this example, we illustrate how to learn a Gromov-Wasserstein dictionary on
 a dataset of structured data such as graphs, denoted
 :math:`\{ \mathbf{C_s} \}_{s \in [S]}` where every nodes have uniform weights.
diff --git a/examples/gromov/plot_partial_fgw.py b/examples/gromov/plot_partial_fgw.py
index cd6976074..ff2c161f4 100644
--- a/examples/gromov/plot_partial_fgw.py
+++ b/examples/gromov/plot_partial_fgw.py
@@ -4,6 +4,9 @@
 Plot partial FGW for subgraph matching
 =================================
 
+.. note::
+    Example added in release: 0.9.6.
+
 This example illustrates the computation of partial (Fused) Gromov-Wasserstein
 divergences for subgraph matching tasks, using the exact formulation $p(F)GW$ and
 the entropically regularized one $p(F)GW_e$ [18, 29].
diff --git a/examples/gromov/plot_quantized_gromov_wasserstein.py b/examples/gromov/plot_quantized_gromov_wasserstein.py
index cdfbb3cd5..dcec838b6 100644
--- a/examples/gromov/plot_quantized_gromov_wasserstein.py
+++ b/examples/gromov/plot_quantized_gromov_wasserstein.py
@@ -4,6 +4,9 @@
 Quantized Fused Gromov-Wasserstein examples
 ===============================================
 
+.. note::
+    Examples added in release: 0.9.4.
+
 These examples show how to use the quantized (Fused) Gromov-Wasserstein
 solvers (qFGW) [68]. POT provides a generic solver `quantized_fused_gromov_wasserstein_partitioned`
 that takes as inputs partitioned graphs potentially endowed with node features,
diff --git a/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py b/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py
index 232da0a56..f5dad0221 100644
--- a/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py
+++ b/examples/gromov/plot_semirelaxed_gromov_wasserstein_barycenter.py
@@ -5,6 +5,9 @@
 Semi-relaxed (Fused) Gromov-Wasserstein Barycenter as Dictionary Learning
 =====================================================
 
+.. note::
+    Example added in release: 0.9.5.
+
 In this example, we illustrate how to learn a semi-relaxed Gromov-Wasserstein
 (srGW) barycenter using a Block-Coordinate Descent algorithm, on a dataset of
 structured data such as graphs, denoted :math:`\{ \mathbf{C_s} \}_{s \in [S]}`
diff --git a/examples/others/plot_EWCA.py b/examples/others/plot_EWCA.py
index af9192537..6bf8e13f5 100644
--- a/examples/others/plot_EWCA.py
+++ b/examples/others/plot_EWCA.py
@@ -4,6 +4,9 @@
 Entropic Wasserstein Component Analysis
 =======================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 This example illustrates the use of EWCA as proposed in [52].
 
 
diff --git a/examples/others/plot_SSNB.py b/examples/others/plot_SSNB.py
index fbc343a8a..80af5685c 100644
--- a/examples/others/plot_SSNB.py
+++ b/examples/others/plot_SSNB.py
@@ -4,6 +4,9 @@
 Smooth and Strongly Convex Nearest Brenier Potentials
 =====================================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 This example is designed to show how to use SSNB [58] in POT.
 SSNB computes an l-strongly convex potential :math:`\varphi` with an L-Lipschitz gradient such that
 :math:`\nabla \varphi \# \mu \approx \nu`. This regularity can be enforced only on the components of a partition
diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py
index f1b9342fa..fe9eaf4ed 100644
--- a/examples/others/plot_WDA.py
+++ b/examples/others/plot_WDA.py
@@ -4,6 +4,9 @@
 Wasserstein Discriminant Analysis
 =================================
 
+.. note::
+    Example added in release: 0.3.0.
+
 This example illustrate the use of WDA as proposed in [11].
 
 
diff --git a/examples/others/plot_WeakOT_VS_OT.py b/examples/others/plot_WeakOT_VS_OT.py
index 16636f1ab..92148a89e 100644
--- a/examples/others/plot_WeakOT_VS_OT.py
+++ b/examples/others/plot_WeakOT_VS_OT.py
@@ -4,6 +4,9 @@
 Weak Optimal Transport VS exact Optimal Transport
 ====================================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 Illustration of 2D optimal transport between distributions that are weighted
 sum of Diracs. The OT matrix is plotted with the samples.
 
diff --git a/examples/others/plot_dmmot.py b/examples/others/plot_dmmot.py
index a493f38fc..d6ea9b878 100644
--- a/examples/others/plot_dmmot.py
+++ b/examples/others/plot_dmmot.py
@@ -4,6 +4,9 @@
 Computing d-dimensional Barycenters via d-MMOT
 ===============================================================================
 
+.. note::
+    Example added in release: 0.9.1.
+
 When the cost is discretized (Monge), the d-MMOT solver can more quickly
 compute and minimize the distance between many distributions without the need
 for intermediate barycenter computations. This example compares the time to
diff --git a/examples/others/plot_factored_coupling.py b/examples/others/plot_factored_coupling.py
index 3aaf5fbf5..55c1db3f9 100644
--- a/examples/others/plot_factored_coupling.py
+++ b/examples/others/plot_factored_coupling.py
@@ -4,6 +4,9 @@
 Optimal transport with factored couplings
 ==========================================
 
+.. note::
+    Example added in release: 0.8.2.
+
 Illustration of the factored coupling OT between 2D empirical distributions
 
 """
diff --git a/examples/others/plot_logo.py b/examples/others/plot_logo.py
index 2710401a0..f69a99c30 100644
--- a/examples/others/plot_logo.py
+++ b/examples/others/plot_logo.py
@@ -4,6 +4,9 @@
 Logo of the POT toolbox
 =======================
 
+.. note::
+    Example added in release: 0.8.2.
+
 In this example we plot the logo of the POT toolbox.
 
 This logo is that it is done 100% in Python and generated using
diff --git a/examples/others/plot_lowrank_GW.py b/examples/others/plot_lowrank_GW.py
index ff1929a68..71cdb4e25 100644
--- a/examples/others/plot_lowrank_GW.py
+++ b/examples/others/plot_lowrank_GW.py
@@ -4,6 +4,9 @@
 Low rank Gromov-Wasterstein between samples
 ========================================
 
+.. note::
+    Example added in release: 0.9.4.
+
 Comparison between entropic Gromov-Wasserstein and Low Rank Gromov Wasserstein [67]
 on two curves in 2D and 3D, both sampled with 200 points.
 
diff --git a/examples/others/plot_lowrank_sinkhorn.py b/examples/others/plot_lowrank_sinkhorn.py
index f48fc873a..7ebad59b5 100644
--- a/examples/others/plot_lowrank_sinkhorn.py
+++ b/examples/others/plot_lowrank_sinkhorn.py
@@ -4,6 +4,9 @@
 Low rank Sinkhorn
 ========================================
 
+.. note::
+    Example added in release: 0.9.2.
+
 This example illustrates the computation of Low Rank Sinkhorn [26].
 
 [65] Scetbon, M., Cuturi, M., & Peyré, G. (2021).
diff --git a/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py b/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py
index 5273fdbb1..b024cea20 100644
--- a/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py
+++ b/examples/others/plot_outlier_detection_with_COOT_and_unbalanced_COOT.py
@@ -4,6 +4,9 @@
 Detecting outliers by learning sample marginal distribution with CO-Optimal Transport and by using unbalanced Co-Optimal Transport
 ======================================================================================================================================
 
+.. note::
+    Example added in release: 0.9.5.
+
 In this example, we consider two point clouds living in different Euclidean spaces, where the outliers
 are artificially injected into the target data. We illustrate two methods which allow to filter out
 these outliers.
diff --git a/examples/others/plot_screenkhorn_1D.py b/examples/others/plot_screenkhorn_1D.py
index b182f02d7..b066dc5b4 100644
--- a/examples/others/plot_screenkhorn_1D.py
+++ b/examples/others/plot_screenkhorn_1D.py
@@ -4,6 +4,9 @@
 Screened optimal transport (Screenkhorn)
 ========================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example illustrates the computation of Screenkhorn [26].
 
 [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).
diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py
index f9d3c2d4b..00b3ad3cc 100644
--- a/examples/plot_OT_1D_smooth.py
+++ b/examples/plot_OT_1D_smooth.py
@@ -4,6 +4,9 @@
 Smooth and sparse OT example
 ================================
 
+.. note::
+    Example updated in release: 0.9.1.
+
 This example illustrates the computation of
 Smooth and Sparse (KL an L2 reg.) OT and
 sparsity-constrained OT, together with their visualizations.
diff --git a/examples/plot_quickstart_guide.py b/examples/plot_quickstart_guide.py
index fb86ac5bd..793f55fce 100644
--- a/examples/plot_quickstart_guide.py
+++ b/examples/plot_quickstart_guide.py
@@ -4,6 +4,8 @@
 Quickstart Guide
 =============================================
 
+.. note::
+    Example added in release: 0.9.6
 
 Quickstart guide to the POT toolbox.
 
diff --git a/examples/sliced-wasserstein/plot_variance.py b/examples/sliced-wasserstein/plot_variance.py
index 58256fd44..f6e01fad0 100644
--- a/examples/sliced-wasserstein/plot_variance.py
+++ b/examples/sliced-wasserstein/plot_variance.py
@@ -4,6 +4,9 @@
 Sliced Wasserstein Distance on 2D distributions
 ===============================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrates the computation of the sliced Wasserstein Distance as
 proposed in [31].
 
diff --git a/examples/sliced-wasserstein/plot_variance_ssw.py b/examples/sliced-wasserstein/plot_variance_ssw.py
index ad31e9cf1..35756bce4 100644
--- a/examples/sliced-wasserstein/plot_variance_ssw.py
+++ b/examples/sliced-wasserstein/plot_variance_ssw.py
@@ -4,6 +4,9 @@
 Spherical Sliced Wasserstein on distributions in S^2
 ====================================================
 
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrates the computation of the spherical sliced Wasserstein discrepancy as
 proposed in [46].
 
diff --git a/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py b/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py
index 4e194c4bf..3a447406b 100644
--- a/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py
+++ b/examples/unbalanced-partial/plot_conv_sinkhorn_ti.py
@@ -4,6 +4,9 @@
 Translation Invariant Sinkhorn for Unbalanced Optimal Transport
 ===============================================================
 
+.. note::
+    Example added in release: 0.9.5.
+
 This examples illustrates the better convergence of the translation
 invariance Sinkhorn algorithm proposed in [73] compared to the classical
 Sinkhorn algorithm.
diff --git a/examples/unbalanced-partial/plot_partial_wass_and_gromov.py b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
index 23a5f96a2..bbed02453 100755
--- a/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
+++ b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py
@@ -4,6 +4,9 @@
 Partial Wasserstein and Gromov-Wasserstein example
 ==================================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example is designed to show how to use the Partial (Gromov-)Wasserstein
 distance computation in POT [29].
 
diff --git a/examples/unbalanced-partial/plot_regpath.py b/examples/unbalanced-partial/plot_regpath.py
index 7f3dab6f7..c66dd2c5c 100644
--- a/examples/unbalanced-partial/plot_regpath.py
+++ b/examples/unbalanced-partial/plot_regpath.py
@@ -3,6 +3,10 @@
 ================================================================
 Regularization path of l2-penalized unbalanced optimal transport
 ================================================================
+
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrate the regularization path for 2D unbalanced
 optimal transport. We present here both the fully relaxed case
 and the semi-relaxed case.
diff --git a/examples/unbalanced-partial/plot_unbalanced_OT.py b/examples/unbalanced-partial/plot_unbalanced_OT.py
index 8351eafd6..0c3fb8442 100644
--- a/examples/unbalanced-partial/plot_unbalanced_OT.py
+++ b/examples/unbalanced-partial/plot_unbalanced_OT.py
@@ -3,6 +3,10 @@
 ==============================================================
 2D examples of exact and entropic unbalanced optimal transport
 ==============================================================
+
+.. note::
+    Example added in release: 0.8.2.
+
 This example is designed to show how to compute unbalanced and
 partial OT in POT.