docs: adapt existing doc to changes fix up

README.md

@@ -230,6 +230,8 @@ Want to know more ? Check the [PyTorch documentation](https://deel-ai.github.io/
 
 There are 4 modules in Xplique, [Attribution methods](https://deel-ai.github.io/xplique/latest/api/attributions/api_attributions/), [Attribution metrics](https://deel-ai.github.io/xplique/latest/api/attributions/metrics/api_metrics/), [Concepts](https://deel-ai.github.io/xplique/latest/api/concepts/cav/), and [Feature visualization](https://deel-ai.github.io/xplique/latest/api/feature_viz/feature_viz/). In particular, the attribution methods module supports a huge diversity of tasks for diverse data types: [Classification](https://deel-ai.github.io/xplique/latest/api/attributions/classification/), [Regression](https://deel-ai.github.io/xplique/latest/api/attributions/regression/), [Object Detection](https://deel-ai.github.io/xplique/latest/api/attributions/object_detection/), and [Semantic Segmentation](https://deel-ai.github.io/xplique/latest/api/attributions/semantic_segmentation/). The methods compatible with such task are highlighted in the following table:
 
+# TEST TABLE, TODO remove
+
 | **Attribution Method** | Type of Model            | Source                                    | Tabular Data | Images               | Time-Series |
 | :--------------------- | :----------------------- | :---------------------------------------- | :----------: | :----------------- : | :---------: |
 | Deconvolution          | TF                       | [Paper](https://arxiv.org/abs/1311.2901)  | C:✔️, R:✔️  | C:✔️, OD:❌, SS:❌ | 🔵          | 
@@ -241,6 +243,22 @@ There are 4 modules in Xplique, [Attribution methods](https://deel-ai.github.io/
 | Deconvolution          | TF                       | [Paper](https://arxiv.org/abs/1311.2901)  | C:Y, R:Y  | C:Y, OD:N, SS:N | WIP          | 
 | Grad-CAM               | TF                       | [Paper](https://arxiv.org/abs/1610.02391) | N          | C:Y, OD:N, SS:N  | N         |
 
+| **Attribution Method** | Type of Model            | Source                                    | Tabular Data | Images               | Time-Series |
+| :--------------------- | :----------------------- | :---------------------------------------- | :----------: | :----------------- : | :---------: |
+| Deconvolution          | TF                       | [Paper](https://arxiv.org/abs/1311.2901)  | C✔️, R✔️  | C✔️, OD❌, SS❌ | 🔵          | 
+| Grad-CAM               | TF                       | [Paper](https://arxiv.org/abs/1610.02391) | ❌          | C✔️, OD❌, SS❌  | ❌         |
+
+| **Attribution Method** | Type of Model            | Source                                    | Tabular Data | Images               | Time-Series |
+| :--------------------- | :----------------------- | :---------------------------------------- | :----------: | :----------------- : | :---------: |
+| Deconvolution          | TF                       | [Paper](https://arxiv.org/abs/1311.2901)  | C✔️ R✔️     | C✔️ OD❌ SS❌      | 🔵          | 
+| Grad-CAM               | TF                       | [Paper](https://arxiv.org/abs/1610.02391) | ❌          | C✔️ OD❌ SS❌       | ❌         |
+
+
+| **Attribution Method** | Type of Model            | Source                                    |
+| :--------------------- | :----------------------- | :---------------------------------------- |
+| Deconvolution          | TF                       | [Paper](https://arxiv.org/abs/1311.2901)  |
+| Grad-CAM               | TF                       | [Paper](https://arxiv.org/abs/1610.02391) |
+
 <details>
 <summary><b>Table of attributions available</b></summary>