v0.8.0

ExplainableAI v0.8.0

Diff since v0.7.0

This release removes the automatic reexport of heatmapping functionality.
Users are now required to manually load VisionHeatmaps.jl and/or TextHeatmaps.jl.

This reduces the maintenance burden for new heatmapping features and the amount of dependencies for users who don't require heatmapping functionality.

• Removed reexport of heatmapping functionality by updating XAIBase dependency (#162).
• Added GradCAM analyzer (#155). Try it with VisionHeatmaps.jl's new heatmap_overlay feature.

Merged pull requests:

• Add GradCAM analyzer (#155) (@JeanAnNess)
• Bump codecov/codecov-action from 3 to 4 (#161) (@dependabot[bot])
• Update XAIBase to v3.0.0 and document heatmap_overlay (#162) (@adrhill)

v0.7.0

ExplainableAI v0.7.0

Diff since v0.6.3

This release moves the core interface (Explanation, heatmap, analyze) into a separate package called XAIBase.jl. Developers can make use of the XAIBase.jl interface to quickly implement or prototype new methods without having to write boilerplate code.

As announced with version v0.6.2, this is first release without LRP, which has been moved to a separate package called RelevancePropagation.jl. This separation is enabled by the new common XAIBase.jl interface.

Notable changes

• Move core interface into XAIBase.jl package (#154).
  • Renamed Explanation field neuron_selection to output_selection
  • Added Explanation field heatmap for heatmapping presets
• Move LRP into RelevancePropagation.jl (#157)
• Remove ImageNet preprocessing utilities (#159)
• Partially move documentation into the Julia-XAI ecosystem documentation

Merged pull requests:

• Move core interface into XAIBase.jl package (#154) (@adrhill)
• Bump actions/cache from 3 to 4 (#156) (@dependabot[bot])
• Move LRP into RelevancePropagation.jl (#157) (@adrhill)
• Remove ImageNet preprocessing utilities (#159) (@adrhill)
• Remove unused dependencies and utils (#160) (@adrhill)

Closed issues:

• LRP rule coverage for Flux layers (#9)
• Add visualization method for language models (#79)
• Add XAIBase dependency (#151)
• Move LRP into separate package (#152)
• Remove ImageNet preprocessing code (#153)
• Update README before 1.0 release (#158)

v0.6.3

ExplainableAI v0.6.3

Diff since v0.6.2

Merged pull requests:

• Allow Gradient analyzers on non-Flux models (#150) (@adrhill)

v0.6.2

ExplainableAI v0.6.2

Diff since v0.6.1

Merged pull requests:

• Add Concept Relevance Propagation (#146) (@adrhill)
• Fix EpsilonPlusFlat and EpsilonAlpha2Beta1Flat composites (#147) (@adrhill)
• Document CRP (#148) (@adrhill)
• Transfer repository to Julia-XAI organization (#149) (@adrhill)

v0.6.1

ExplainableAI v0.6.1

Diff since v0.6.0

Merged pull requests:

• Bump actions/checkout from 3 to 4 (#139) (@dependabot[bot])
• Fix neuron selection on GPUs (#140) (@adrhill)
• Make Tullio weak dependency using package extensions (#141) (@adrhill)
• Fix ones_like on GPUs (#142) (@adrhill)
• Improve LRP backward pass (#143) (@adrhill)
• GPU support for gradient analyzers (#144) (@adrhill)
• Document GPU support (#145) (@adrhill)

Closed issues:

• Test for type stability of analyzers (#34)
• Use multithreading in batch gradient computation (#63)

v0.6.0

ExplainableAI v0.6.0

Diff since v0.5.7

Closed issues:

• Add LRP support for Parallel layer (#10)
• Document use of LoopVectorization.jl and CUDA.jl (#64)
• Add LRP support for nested Chains (#90)
• Add generalized Gamma rule (#91)
• Rename composite primitives (#120)
• Add composite primitive to assign rule at specific position in model (#121)
• Support BatchNorm layers in LRP (#122)
• Refactor results struct (#123)
• Add Aqua.jl tests (#124)
• Update canonizer to support nested Flux Chains (#132)
• Update documentation for v0.6.0 release (#133)

Merged pull requests:

• Add GeneralizedGammaRule (#109) (@adrhill)
• Simplify LRP analyzer and clean-up rule default parameters (#110) (@adrhill)
• Simplify LRP model checks (#112) (@adrhill)
• Update dependencies (#116) (@adrhill)
• Bump actions/cache from 1 to 3 (#117) (@dependabot[bot])
• Bump actions/checkout from 2 to 3 (#118) (@dependabot[bot])
• Support nested Flux Chains in LRP (#119) (@adrhill)
• Add Aqua.jl tests (#125) (@adrhill)
• Refactor Explanation struct (#126) (@adrhill)
• Faster tests and benchmarks (#127) (@adrhill)
• Set LRP output relevance to one (#128) (@adrhill)
• Enable BatchNorm layers in LRP (#129) (@adrhill)
• Rename composite primitives (#130) (@adrhill)
• Support nested indexing in composite primitive LayerMap (#131) (@adrhill)
• Add PassRule on normalization layers to composite presets, improve show (#134) (@adrhill)
• Support Parallel layers in LRP (#135) (@adrhill)
• Rename Explanation field attribution to val (#136) (@adrhill)
• Update documentation for v0.6.0 (#137) (@adrhill)
• Support canonization of Parallel layers (#138) (@adrhill)

v0.5.7

ExplainableAI v0.5.7

Diff since v0.5.6

Merged pull requests:

• CompatHelper: bump compat for PrettyTables to 2, (keep existing compat) (#101) (@github-actions[bot])

v0.5.6

ExplainableAI v0.5.6

Diff since v0.5.5

Merged pull requests:

• Drop Flux v0.12 compatibility (#99) (@adrhill)

v0.5.5

ExplainableAI v0.5.5

Diff since v0.5.4

Merged pull requests:

• Update ReferenceTests to v0.10 (#95) (@adrhill)
• Fast FlatRule (#96) (@adrhill)
• Add bias to layers in LRP rule tests (#97) (@adrhill)
• Fast WSquareRule on Dense layers (#98) (@adrhill)

v0.5.4

ExplainableAI v0.5.4

Diff since v0.5.3

Closed issues:

• Add printing of LRP analyzers, showing layers and rules (#83)

Merged pull requests:

• Implement Base.show for LRP analyzers (#89) (@adrhill)
• Fix bug in LRP rules (#92) (@adrhill)
• Fix input modification for FlatRule and WSquareRule (#93) (@adrhill)
• Update tests and references (#94) (@adrhill)