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 LRP technique by Bach et al. (2015).

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 function.

- F Horn, L Arras, G Montavon, KR Müller, W Samek. Discovering topics in text datasets by visualizing relevant words

arXiv, 18 Jul 2017 - F Horn, L Arras, G Montavon, KR Müller, W Samek. Exploring text datasets by visualizing relevant words

arXiv, 17 Jul 2017 - PJ Kindermans, K Schütt, M Alber, KR Müller, S Dähne. PatternNet and PatternLRP - Improving the interpretability of neural networks

arXiv, 16 May 2017 [bibtex]

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

Digital Signal Processing, 73:1-15, 2017 [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, 1:1-10, 2017 [preprint, bibtex] - 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] - 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] - 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] - 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] - 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] - 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]

- 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 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] - 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] - 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] - 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), 2271-75, 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), 6679:913-22, Springer Singapore, 2016 [preprint, bibtex]

- S Lapuschkin, A Binder, KR Müller, W Samek. Understanding and Comparing Deep Neural Networks for Age and Gender Classification

ICCV Workshop on Analysis and Modeling of Faces and Gestures, 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] - 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] - 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] - 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]