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Tutorial on Interpreting and Explaining Deep Models in Computer Vision Wojciech Samek Grgoire Montavon Klaus-Robert Mller (Fraunhofer HHI) (TU Berlin) (TU Berlin) 08:30 - 09:15 Introduction KRM 09:15 - 10:00 Techniques for Interpretability

  1. Tutorial on Interpreting and Explaining Deep Models in Computer Vision Wojciech Samek Grégoire Montavon Klaus-Robert Müller (Fraunhofer HHI) (TU Berlin) (TU Berlin) 08:30 - 09:15 Introduction KRM 09:15 - 10:00 Techniques for Interpretability GM 10:00 - 10:30 Coffee Break ALL 10:30 - 11:15 Applications of Interpretability WS 11:15 - 12:00 Further Applications and Wrap-Up KRM

  2. Why interpretability?

  3. Why interpretability?

  4. Why interpretability?

  5. Why interpretability?

  6. Why interpretability? Insights!

  7. Why interpretability?

  8. Overview and Intuition for different Techniques: sensitivity, deconvolution, LRP and friends.

  9. Understanding Deep Nets: Two Views Understanding what mechanism Understanding how the network the network uses to solve a relates the input to the output problem or implement a function. variables.

  11. Approach 1: Class Prototypes “How does a goose typically look like according to the neural network?” non-goose goose Class prototypes Image from Symonian’13

  12. Approach 2: Individual Explanations “Why is a given image classified as a sheep?” non-sheep sheep Images from Lapuschkin’ 16

  13. 3. Sensitivity analysis input evidence for “car” DNN Sensitivity analysis: The relevance of input feature i is given by the squared partial derivative:

  14. Understanding Sensitivity Analysis Sensitivity analysis: Problem: sensitivity analysis does not highlight cars Observation: Sensitivity analysis explains a variation of the function, not the function value itself.

  15. Sensitivity Analysis Problem: Shattered Gradients [Montufar’ 14 , Balduzzi’ 17] Input gradient (on which sensitivity analysis is based), becomes increasingly highly varying and unreliable with neural network depth.

  16. Shattered Gradients II [Montufar’ 14 , Balduzzi’ 17] Input gradient (on which sensitivity analysis is based), becomes increasingly highly varying and unreliable with neural network depth. Example in [0,1]:

  17. LPR is not sensitive to gradient shattering

  18. Explaining Neural Network Predictions Layer-wise relevance Propagation (LRP, Bach et al 15 ) first method to explain nonlinear classifiers - based on generic theory (related to Taylor decomposition – deep taylor decomposition M et al 16 ) - applicable to any NN with monotonous activation, BoW models, Fisher Vectors, SVMs etc. Explanation : “ Which pixels contribute how much to the classification ” ( Bach et al 2015 ) (what makes this image to be classified as a car) Sensitivity / Saliency : “ Which pixels lead to increase/decrease of prediction score when changed ” (what makes this image to be classified more/less as a car) (Baehrens et al 10, Simonyan et al 14 ) Cf. Deconvolution : “Matching input pattern for the classified object in the image ” ( Zeiler & Fergus 2014 ) (relation to f(x) not specified) Each method solves a different problem!!!

  19. Explaining Neural Network Predictions Classification large activation cat ladybug dog

  20. Explaining Neural Network Predictions Explanation cat ladybug dog Initialization =

  21. Explaining Neural Network Predictions Explanation ? cat ladybug dog Theoretical interpretation Deep Taylor Decomposition depends on the activations and the weights: LRP naive z-rule

  22. Explaining Neural Network Predictions Explanation large relevance cat ladybug dog Relevance Conservation Property

  23. Historical remarks on Explaining Predictors Gradients Sensitivity Gradient/vs./Decomposition (Baehrens&et&al.&2010) (Montavon&et&al.,&2018) Sensitivity Sensitivity (Morch&et&al.,&1995) (Simonyan&et&al.&2014) Gradient/times/input/ DeepLIFT GradKCAM Integrated/Gradient/ (Shrikumar&et&al.,&2016) (Shrikumar&et&al.,&2016) (Selvaraju&et&al.,&2016) (Sundararajan&et&al.,&2017) Decomposition LRP/for/LSTM LRP (Arras&et&al.,&2017) Probabilistic/Diff (Bach&et&al.,&2015) (Zintgraf&et&al.,&2016) Excitation/Backprop (Zhang&et&al.,&2016) Deep/Taylor/Decomposition (Montavon&et&al.,&2017&(arXiv&2015)) Optimization Meaningful/Perturbations LIME PatternLRP (Fong&&&Vedaldi 2017) (Ribeiro&et&al.,&2016) (Kindermans&et&al.,&2017) Deconvolution Guided/Backprop Deconvolution (Springenberg&et&al.&2015) (Zeiler&&&Fergus&2014) Understanding/the/Model TCAV Deep/Visualization (Kim&et&al.&2018) Synthesis/of/preferred/inputs (Yosinski&et&al.,&2015) Inverting/CNNs (Nguyen&et&al.&2016) Feature/visualization (Dosovitskiy&&&Brox,&2015) (Erhan&et&al.&2009) Network/Dissection Inverting/CNNs RNN/cell/state/analysis (Zhou&et&al.&2017) (Mahendran&&&Vedaldi,&2015) (Karpathy&et&al.,&2015)

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