kontakt@camo.nrw +49 202 / 439 1164

[Paper] Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation

SAIAD 2021

In this paper, we propose a method for post-hoc explainability of black-box models. The key component of the semantic and quantitative local explanation is a knowledge distillation (KD) process which is used to mimic the teacher’s behavior by means of an explainable generative model. Therefore, we introduce a Concept Probability Density Encoder (CPDE) in conjunction with a Gaussian Discriminant Decoder (GDD) to describe the contribution of high-level concepts (e.g. object parts, color, shape). We argue that our objective function encourages both, an explanation given by a set of likelihood ratios and a measure to describe how far the explainer deviates from the training data distribution of the concepts. The method can leverage any pre-trained concept classifier that admits concept scores (e.g. logits) or probabilities. We demonstrate the effectiveness of the proposed method in the context of object detection utilizing the DensePose dataset.


Verwandte Arbeiten

[Paper] A Wizard of Oz Field Study to Understand Non-Driving-Related Activities, Trust, and Acceptance of Automated Vehicles

Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based […]

Mehr erfahren

[Paper] Multivariate Confidence Calibration for Object Detection

Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object […]

Mehr erfahren

[Paper] “Help, Accident Ahead!”: Using Mixed Reality Environments in Automated Vehicles to Support Occupants After Passive Accident Experiences

Currently, car assistant systems mainly try to prevent accidents. Increasing built-in car technology also extends the potential applications in vehicles. Future cars might have virtual windshields that augment the traffic or individual virtual assistants interacting […]

Mehr erfahren