Optimizing Explanations by Network Canonization and Hyperparameter Search
Rule-based and modified backpropagation XAI methods struggle with innovative layer building blocks and implementation-invariance issues.
In this work we propose canonizations for popular deep neural network architectures and...
Experimental Demonstration of Optical Modulation Format Identification Using SOI-based Photonic Reservoir
We experimentally show modulation format identification in the optical domain using Silicon-on-Insulator-based Photonic-Integrated-Circuit (PIC) reservoir. Identification of 32 GBd single-polarization signals of 4QAM, 16QAM, 32QAM and 64QAM is...
Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes
Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we...
Assessing the Value of Multimodal Interfaces: A Study on Human–Machine Interaction in Weld Inspection Workstations
Multimodal user interfaces promise natural and intuitive human–machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This...
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded...
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or...
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To...
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating the performance of these explanation methods for neural networks,...
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically to...
Sydnone Methides: Intermediates between Mesoionic Compounds and Mesoionic N-Heterocyclic Olefins
Sydnone methides represent an almost unknown class of mesoionic compounds which possess exocyclic carbon substituents instead of oxygen (sydnones) or nitrogen (sydnone imines) in the 5-position of a 1,2,3-oxadiazolium ring. Unsubstituted...