Papers › Learning Important Features Through Propagating Activation Differences

Learning Important Features Through Propagating Activation Differences

10 Apr 2017ICML 2017 8arXiv:1704.02685archive 2025-07-28

Avanti Shrikumar, Peyton Greenside, Anshul Kundaje

The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, ICML slides: bit.ly/deeplifticmlslides, ICML talk: https://vimeo.com/238275076, code: http://goo.gl/RM8jvH.

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adrhill/explainableai.jl mentioned on GitHubpytorch report
ascillitoe/shap mentioned on GitHubtf report
austinbrown34/shap mentioned on GitHubtf report
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DeepLift vlue-c/Visual-Explanation-Methods-PyTorch/torchvex/contrib/deeplift.py community (archive-listed) ran fingerprinted MIT (permissive) · f70e444e11ee8d3a · report
ExplanationMethod vlue-c/Visual-Explanation-Methods-PyTorch/torchvex/contrib/deeplift.py community (archive-listed) ran MIT (permissive) · baff092b503bcfbe · report
PyTorchDeepExplainer austinbrown34/shap/shap/explainers/deep/deep_pytorch.py community (archive-listed) ran MIT (permissive) · 17c52f66fb9f49e0 · report
PyTorchDeepExplainer saivarunr/xshap/shap/explainers/deep/deep_pytorch.py community (archive-listed) ran MIT (permissive) · 315e69a6733bc503 · report
backward_walk_ops shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 174664b8521b7367 · report
forward_walk_ops shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 556ef4411f7dfff0 · report
tensors_blocked_by_false shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 42c14f4de4f2d1ba · report
Explainer shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) unverified MIT (permissive) · 6cddc3a37d231206 · report
TFDeepExplainer shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) unverified MIT (permissive) · b802ce233152291a · report
add_interim_values austinbrown34/shap/shap/explainers/deep/deep_pytorch.py community (archive-listed) unverified MIT (permissive) · 034debadf477812f · report
break_dependence shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) unverified MIT (permissive) · d30fb55d50e88ecc · report
get_target_input austinbrown34/shap/shap/explainers/deep/deep_pytorch.py community (archive-listed) unverified MIT (permissive) · 52628a5c5ffac3aa · report
passthrough shaoshanglqy/shap-shapley/shap/explainers/deep/deep_tf.py community (archive-listed) unverified MIT (permissive) · 70789f676e5840c3 · report
deeplift_grad identical code first harvested elsewhere ran · violated contract licence of this copy not recorded · 2f372a731d99a85b · report
deeplift_tensor_grad identical code first harvested elsewhere unverified licence of this copy not recorded · 10adb065b8e203e6 · report

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