This webpage aims to regroup publications and software produced as part of a joint project at Fraunhofer HHI, TU Berlin and SUTD Singapore on developing new method to understand nonlinear predictions of state-of-the-art machine learning models.

Machine learning models, in particular deep neural networks (DNNs), are characterized by very high predictive power, but in many case, are not easily interpretable by a human. Interpreting a nonlinear classifier is important to gain trust into the prediction, and to identify potential data selection biases or artefacts.

The project studies in particular techniques to decompose the prediction in terms of contributions of individual input variables such that the produced decomposition (i.e. explanation) can be visualized in the same way as the input data.

Draw a handwritten digit and see the heatmap being formed in real-time. Create your own heatmap for natural images or text. These demos are based on the Layer-wise Relevance Propagation (LRP) technique by Bach et al. (2015).

Layer-wise Relevance Propagation (LRP) is a method that identifies important pixels by running a backward pass in the neural network. The backward pass is a conservative relevance redistribution procedure, where neurons that contribute the most to the higher-layer receive most relevance from it. The LRP procedure is shown graphically in the figure below.

The method can be easily implemented in most programming languages and integrated to existing neural network frameworks. When applied to deep ReLU networks, LRP can be understood as a Deep Taylor Decomposition of the prediction.

- Keras Explanation Toolbox (LRP and other Methods)
- GitHub project page for the LRP Toolbox
- TensorFlow LRP Wrapper
- LRP Code for LSTM

Videos:

Introduction |
Methods |
Applications 1 |
Applications 2 |

- MICCAI 2018 Tutorial (Website | Slides)

Explainable ML, Medical Applications -
Talk at Intl. Workshop ML & AI 2018 (Slides)

Deep Taylor Decomposition, Validating Explanations -
WCCI 2018 Keynote (Slides)

Explainable ML, LRP, Applications - GCPR 2017 Tutorial (Slides)
- ICASSP 2017 Tutorial (Slides 1, 2, 3)

- G Montavon, W Samek, KR Müller. Methods for Interpreting and Understanding Deep Neural Networks

Digital Signal Processing, 73:1-15, 2018 [preprint | bibtex] - W Samek, T Wiegand, KR Müller. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

ITU Journal: ICT Discoveries - Special Issue 1 - The Impact of AI on Communication Networks and Services, 1(1):39-48, 2018 [preprint, bibtex]

- S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek. On Pixel-wise Explanations for Non-Linear Classifier Decisions by Layer-wise Relevance Propagation

PLOS ONE, 10(7):e0130140, 2015 [preprint, bibtex] - G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller. Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

Pattern Recognition, 65:211–222, 2017 [preprint, bibtex] - L Arras, G Montavon, KR Müller, W Samek. Explaining Recurrent Neural Network Predictions in Sentiment Analysis

EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 159-168, 2017 [preprint, bibtex] - A Binder, G Montavon, S Lapuschkin, KR Müller, W Samek. Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Artificial Neural Networks and Machine Learning – ICANN 2016, Part II, Lecture Notes in Computer Science, Springer-Verlag, 9887:63-71, 2016 [preprint, bibtex] - PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne. Learning how to explain neural networks: PatternNet and PatternAttribution

International Conference on Learning Representations (ICLR), 2018 - J Kauffmann, KR Müller, G Montavon. Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models

arXiv:1805.06230, 2018

- W Samek, A Binder, G Montavon, S Bach, KR Müller. Evaluating the Visualization of What a Deep Neural Network has Learned

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 28(11):2660-2673, 2017 [preprint, bibtex]

- M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, W Samek, KR Müller, S Dähne, PJ Kindermans iNNvestigate neural networks!.

arXiv:1808.04260, 2018 - S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks

Journal of Machine Learning Research (JMLR), 17(114):1−5, 2016 [preprint, bibtex]

- I Sturm, S Bach, W Samek, KR Müller. Interpretable Deep Neural Networks for Single-Trial EEG Classification

Journal of Neuroscience Methods, 274:141–145, 2016 [preprint, bibtex] - A Binder, M Bockmayr, M Hägele, S Wienert, D Heim, K Hellweg, A Stenzinger, L Parlow, J Budczies, B Goeppert, D Treue, M Kotani, M Ishii, M Dietel, A Hocke, C Denkert, KR Müller, F Klauschen. Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv:1805.11178, 2018 - F Horst, S Lapuschkin, W Samek, KR Müller, WI Schöllhorn. What is Unique in Individual Gait Patterns?

arXiv:1808.04308, 2018

- L Arras, F Horn, G Montavon, KR Müller, W Samek. "What is Relevant in a Text Document?": An Interpretable Machine Learning Approach

PLOS ONE, 12(8):e0181142, 2017 [preprint, bibtex] - L Arras, G Montavon, KR Müller, W Samek. Explaining Recurrent Neural Network Predictions in Sentiment Analysis

EMNLP Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 159-168, 2017 [preprint, bibtex] - L Arras, F Horn, G Montavon, KR Müller, W Samek. Explaining Predictions of Non-Linear Classifiers in NLP

ACL Workshop on Representation Learning for NLP, 1-7, 2016 [preprint, bibtex] - F Horn, L Arras, G Montavon, KR Müller, W Samek. Exploring text datasets by visualizing relevant words

arXiv:1707.05261, 2017

- S Lapuschkin, A Binder, G Montavon, KR Müller, W Samek. Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2912-2920, 2016 [preprint, bibtex] - S Lapuschkin, A Binder, KR Müller, W Samek. Understanding and Comparing Deep Neural Networks for Age and Gender Classification

IEEE International Conference on Computer Vision Workshops (ICCVW), 1629-1638, 2017 [preprint, bibtex] - C Seibold, W Samek, A Hilsmann, P Eisert. Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks

arXiv:1806.04265, 2018 - S Bach, A Binder, KR Müller, W Samek. Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth

Proceedings of the IEEE International Conference on Image Processing (ICIP), 2271-2275, 2016 [preprint, bibtex] - A Binder, S Bach, G Montavon, KR Müller, W Samek. Layer-wise Relevance Propagation for Deep Neural Network Architectures

Proceedings of the 7th International Conference on Information Science and Applications (ICISA), 6679:913-922, Springer Singapore, 2016 [preprint, bibtex] - F Arbabzadah, G Montavon, KR Müller, W Samek. Identifying Individual Facial Expressions by Deconstructing a Neural Network

Pattern Recognition - 38th German Conference, GCPR 2016, Lecture Notes in Computer Science, 9796:344-354, 2016 [preprint, bibtex]

- C Anders, G Montavon, W Samek, KR Müller. Understanding Patch-Based Learning by Explaining Predictions

arXiv:1806.06926, 2018 - V Srinivasan, S Lapuschkin, C Hellge, KR Müller, W Samek. Interpretable human action recognition in compressed domain

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1692-1696, 2017 [preprint, bibtex]

- S Becker, M Ackermann, S Lapuschkin, KR Müller, W Samek. Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals

arXiv:1807.03418, 2018

- W Samek, G Montavon, A Binder, S Lapuschkin, and KR Müller. Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation

NIPS Workshop on Interpretable ML for Complex Systems, 1-5, 2016 [preprint, bibtex] - G Montavon, S Bach, A Binder, W Samek, KR Müller. Deep Taylor Decomposition of Neural Networks

ICML Workshop on Visualization for Deep Learning, 1-3, 2016 [preprint, bibtex] - A Binder, W Samek, G Montavon, S Bach, KR Müller. Analyzing and Validating Neural Networks Predictions

ICML Workshop on Visualization for Deep Learning, 1-4, 2016 [preprint, bibtex]

- Pascal VOC 2012 Multilabel Model: [caffemodel] [prototxt]