diff --git a/README.md b/README.md
index 35e24655..fbc3c66b 100644
--- a/README.md
+++ b/README.md
@@ -287,6 +287,7 @@ TF : Tensorflow compatible
| Testing CAV (TCAV) | TF | [Paper](https://arxiv.org/pdf/1711.11279.pdf) |
| (WIP) Robust TCAV | |
| (WIP) Automatic Concept Extraction (ACE) |
+| Concept Recursive Activation FacTorization for Explainability (Craft) | TF | [Paper](https://arxiv.org/pdf/2211.10154.pdf) |
TF : Tensorflow compatible
diff --git a/docs/api/concepts/craft.md b/docs/api/concepts/craft.md
new file mode 100644
index 00000000..4e628d0e
--- /dev/null
+++ b/docs/api/concepts/craft.md
@@ -0,0 +1,50 @@
+# CRAFT
+
+CRAFT or Concept Recursive Activation FacTorization for Explainability is a method for automatically extracting human-interpretable concepts from deep networks.
+
+This concept activations factorization method aims to explain a pre-trained model's decisions both on a per-class and per-image basis by highlighting both "what" the model saw and “where” it saw it.
+
+It is made up from 3 ingredients:
+1. a method to recursively decompose concepts into sub-concepts
+2. a method to better estimate the importance of extracted concepts
+3. a method to use any attribution method to create concept attribution maps, using implicit differentiation
+
+CRAFT requires splitting the model in two parts: $(g, h)$ such that $f(x) = (g \cdot h)(x)$. To put it simply, $g$ is the function that maps our input to the latent space (the penultimate layer of our model), and $h$ is the function that maps this penultimate layer to the output. It is important to note that if the model contains a global average pooling layer, it is strongly recommended to provide CRAFT with the layer before the global average pooling.
+
+!!!warning
+ Please keep in mind that the activations must be positives (after relu or any positive activation function)
+
+
+## Example
+
+```python
+from xplique.concepts import CraftTf as Craft
+from xplique.plots.craft_visualizations import CraftPlot
+
+# cut the model in two parts (as explained in the paper)
+# first part is g(.) our 'input_to_latent' model, second part is h(.) our 'latent_to_logit' model
+g = tf.keras.Model(model.input, model.layers[-3].output)
+h = tf.keras.Model(model.layers[-2].input, model.layers[-1].output)
+
+# Create a Craft concept extractor from these 2 models
+craft = Craft(input_to_latent = g,
+ latent_to_logit = h,
+ number_of_concepts = 10,
+ patch_size = 80,
+ batch_size = 64)
+
+# Use Craft to get the crops (crops), the embedding of the crops (crops_u), and the concept bank (w)
+# 330 is the rabbit class id in imagenet
+crops, crops_u, w = craft.fit(images_preprocessed, class_id=rabbit_class)
+
+# Compute Sobol indices to understand which concept matters
+importances = craft.estimate_importance(nb_most_important_concepts=5)
+
+# Display those concepts by showing the 10 best crops for each concept
+craft.plot_concepts_crops(nb_crops=10)
+
+```
+
+{{xplique.concepts.craft.Craft}}
+
+[^1]: [CRAFT: Concept Recursive Activation FacTorization for Explainability (2023).](https://arxiv.org/pdf/2211.10154.pdf)
diff --git a/docs/assets/craft.jpeg b/docs/assets/craft.jpeg
new file mode 100644
index 00000000..743144a0
Binary files /dev/null and b/docs/assets/craft.jpeg differ
diff --git a/docs/index.md b/docs/index.md
index f249772f..28ab6df2 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -85,6 +85,17 @@ Finally, the _Metrics_ module covers the current metrics used in explainability.
+ - [**Concepts Methods**: CRAFT: Getting started on Tensorflow](https://colab.research.google.com/drive/1Fk0EObQQfwAtiiFQyClX31HiHgrEzN9_)
+ [](https://colab.research.google.com/drive/1Fk0EObQQfwAtiiFQyClX31HiHgrEzN9_)
+ , or [on Pytorch](https://colab.research.google.com/drive/1_8l5bwzalKBvFkrv5Lvcph97FtUdd3Wp)
+ [](https://colab.research.google.com/drive/1_8l5bwzalKBvFkrv5Lvcph97FtUdd3Wp)
+
+
+
+ 
+
+
+
- [**Feature Visualization**: Getting started](https://colab.research.google.com/drive/1st43K9AH-UL4eZM1S4QdyrOi7Epa5K8v) [](https://colab.research.google.com/drive/1st43K9AH-UL4eZM1S4QdyrOi7Epa5K8v)
- [**Feature Visualization**: Getting started](https://colab.research.google.com/drive/1st43K9AH-UL4eZM1S4QdyrOi7Epa5K8v) [](https://colab.research.google.com/drive/1st43K9AH-UL4eZM1S4QdyrOi7Epa5K8v)
@@ -267,7 +278,7 @@ Now that Xplique is installed, here are 4 basic examples of what you can do with
| Testing CAV (TCAV) | TF | [Paper](https://arxiv.org/pdf/1711.11279.pdf) |
| (WIP) Robust TCAV | |
| (WIP) Automatic Concept Extraction (ACE) |
-
+ | Concept Recursive Activation FacTorization for Explainability (Craft) | TF | [Paper](https://arxiv.org/pdf/2211.10154.pdf) |
TF : Tensorflow compatible
??? abstract "Table of Feature Visualization methods available"
diff --git a/docs/tutorials.md b/docs/tutorials.md
index 6dd83321..1b70609c 100644
--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -60,7 +60,10 @@ Here is the lists of the availables tutorial for now:
## Concepts extraction
-**WIP**
+| Category | **Tutorial Name** | Notebook |
+|:------------- | :--------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| CRAFT | CRAFT Tensorflow | [](https://colab.research.google.com/drive/1Fk0EObQQfwAtiiFQyClX31HiHgrEzN9_) |
+| CRAFT | CRAFT Pytorch | [](https://colab.research.google.com/drive/1_8l5bwzalKBvFkrv5Lvcph97FtUdd3Wp) |
## Features Visualizations
diff --git a/mkdocs.yml b/mkdocs.yml
index 3a2bfdb2..0f504121 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -39,6 +39,7 @@ nav:
- Concept based:
- Cav: api/concepts/cav.md
- Tcav: api/concepts/tcav.md
+ - Craft: apt/concepts/craft.md
- Feature visualization:
- Modern Feature Visualization (MaCo): api/feature_viz/maco.md
- Feature visualization: api/feature_viz/feature_viz.md