# In-Distribution Interpretability for Challenging Modalities

@article{Hei2020InDistributionIF, title={In-Distribution Interpretability for Challenging Modalities}, author={Cosmas Hei{\ss} and Ron Levie and Cinjon Resnick and Gitta Kutyniok and Joan Bruna}, journal={ArXiv}, year={2020}, volume={abs/2007.00758} }

It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the mode of operation of such models has advanced rapidly in the past few years. Recent work introduced an intuitive framework which utilizes generative models to improve on the meaningfulness of such explanations. In this work, we display the flexibility of this method to interpret diverse and challenging modalities…

## 4 Citations

Fast Hierarchical Games for Image Explanations

- Computer ScienceArXiv
- 2021

This work presents a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients – h-Shap – that resolves some of the limitations of current approaches and is scalable and can be computed without the need of approximation.

A Rate-Distortion Framework for Explaining Black-box Model Decisions

- Computer SciencexxAI@ICML
- 2020

The RDE framework is a mathematically well-founded method for explaining black-box model decisions based on perturbations of the target input signal and applies to any differentiable pre-trained model such as neural networks.

Sparsest Univariate Learning Models Under Lipschitz Constraint

- Computer Science, MathematicsIEEE Open Journal of Signal Processing
- 2022

This work proposes continuous-domain formulations for one-dimensional regression problems that admit global minimizers that are continuous and piecewise-linear (CPWL) functions and proposes efficient algorithms that find the sparsest solution of each problem: the CPWL mapping with the least number of linear regions.

Cartoon Explanations of Image Classifiers

- Computer ScienceArXiv
- 2021

This work presents CartoonX (Cartoon Explanation), a novel model-agnostic explanation method tailored towards image classifiers and based on the rate-distortion explanation (RDE) framework, and demonstrates that CartoonX can reveal novel valuable explanatory information, particularly for misclassifications.

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