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08/23/2023
Layer-wise Feedback Propagation
In this paper, we present Layer-wise Feedback Propagation (LFP), a novel...
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08/18/2023
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space
Deep Neural Networks are prone to learning spurious correlations embedde...
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04/27/2023
XAI-based Comparison of Input Representations for Audio Event Classification
Deep neural networks are a promising tool for Audio Event Classification...
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04/12/2023
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models
Recent work in XAI for eye tracking data has evaluated the suitability o...
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03/22/2023
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 correlatio...
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03/11/2023
Explainable AI for Time Series via Virtual Inspection Layers
The field of eXplainable Artificial Intelligence (XAI) has greatly advan...
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02/14/2023
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Explainable AI (XAI) is a rapidly evolving field that aims to improve tr...
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11/30/2022
Optimizing Explanations by Network Canonization and Hyperparameter Search
Explainable AI (XAI) is slowly becoming a key component for many AI appl...
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11/22/2022
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 trustw...
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11/21/2022
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations
Applying traditional post-hoc attribution methods to segmentation or obj...
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06/07/2022
From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation
The emerging field of eXplainable Artificial Intelligence (XAI) aims to ...
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05/04/2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
The ability to continuously process and retain new information like we d...
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03/18/2022
But that's not why: Inference adjustment by interactive prototype deselection
Despite significant advances in machine learning, decision-making of art...
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03/15/2022
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
Explainable Artificial Intelligence (XAI) is an emerging research field ...
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02/14/2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations
The evaluation of explanation methods is a research topic that has not y...
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02/14/2022
Measurably Stronger Explanation Reliability via Model Canonization
While rule-based attribution methods have proven useful for providing lo...
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02/07/2022
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging
State-of-the-art machine learning models are commonly (pre-)trained on l...
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09/09/2021
ECQ^x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
The remarkable success of deep neural networks (DNNs) in various applica...
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06/24/2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Deep Neural Networks (DNNs) are known to be strong predictors, but their...
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07/17/2020
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Cross-domain few-shot classification task (CD-FSC) combines few-shot cla...
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04/22/2020
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
Integrated gradients as an attribution method for deep neural network mo...
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03/17/2020
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
With the broader and highly successful usage of machine learning in indu...
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01/04/2020
Understanding Image Captioning Models beyond Visualizing Attention
This paper explains predictions of image captioning models with attentio...
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12/22/2019
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed
Today's machine learning models for computer vision are typically traine...
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12/18/2019
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applicati...
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12/16/2019
On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence
Systems incorporating Artificial Intelligence (AI) and machine learning ...
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10/22/2019
Towards best practice in explaining neural network decisions with LRP
Within the last decade, neural network based predictors have demonstrate...
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08/15/2019
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Deep learning has recently gained popularity in digital pathology due to...
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02/26/2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application prob...
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08/13/2018
What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis
Machine learning (ML) techniques such as (deep) artificial neural networ...
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08/13/2018
iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many applicati...
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07/09/2018
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Interpretability of deep neural networks is a recently emerging area of ...
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08/25/2017