ExplainableAI v0.6.2

Author: adrhill

CHANGELOG.md

@@ -1,4 +1,15 @@
 # ExplainableAI.jl
+## Version `v0.6.2`
+This is first release of ExplainableAI.jl as part of the 
+[Julia-XAI](https://github.com/Julia-XAI) organization ([#149][pr-149])
+and the last release that includes LRP before it is moved to its own separate package.
+
+- ![Feature][badge-feature] Add Concept Relevance Propagation analyzer `CRP` ([#146][pr-146], [#148][pr-148])
+- ![Feature][badge-feature] Add option to process heatmaps batch-wise 
+  using keyword argument `process_batch=true` ([#146][pr-146], [#148][pr-148])
+- ![Bugfix][badge-bugfix] Remove `FlatRule` on dense layers 
+  from `EpsilonPlusFlat` and `EpsilonAlpha2Beta1Flat` composite presets ([#147][pr-147])
+
 ## Version `v0.6.1`
 This release brings GPU support to all analyzers.
 - ![Feature][badge-feature] Support LRP on GPUs ([#142][pr-142], [#140][pr-140])
@@ -165,6 +176,10 @@ Performance improvements:
 ![Maintenance][badge-maintenance]
 ![Documentation][badge-docs]
 -->
+[pr-149]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/149
+[pr-148]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/148
+[pr-147]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/147
+[pr-146]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/146
 [pr-145]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/145
 [pr-144]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/144
 [pr-142]: https://github.com/Julia-XAI/ExplainableAI.jl/pull/142

Project.toml

@@ -1,7 +1,7 @@
 name = "ExplainableAI"
 uuid = "4f1bc3e1-d60d-4ed0-9367-9bdff9846d3b"
 authors = ["Adrian Hill"]
-version = "0.6.1"
+version = "0.6.2"
 
 [deps]
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"

README.md

@@ -8,7 +8,7 @@ ___
 Explainable AI in Julia.
 
 This package implements interpretability methods for black box models,
-with a focus on local explanations and attribution maps.
+with a focus on local explanations and attribution maps in input space.
 It is similar to [Captum][captum-repo] and [Zennit][zennit-repo] for PyTorch 
 and [iNNvestigate][innvestigate-repo] for Keras models.