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Found 23 papers, 13 papers with code
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks.
But that's not why: Inference adjustment by interactive prototype deselection
It shows that even correct classifications can rely on unreasonable prototypes that result from confounding variables in a dataset.
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
We conclude that while model improvement based on XAI can have significant beneficial effects even on complex and not easily quantifyable model properties, these methods need to be applied carefully, since their success can vary depending on a multitude of factors, such as the model and dataset used, or the employed explanation method.
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness.
Measurably Stronger Explanation Reliability via Model Canonization
While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields new challenges in generating trustworthy explanations, since the established rule sets might not be sufficient or applicable to novel network structures.
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging
We demonstrate that pattern-based artifact modeling has beneficial effects on the application of CAVs as a means to remove influence of confounding features from models via the ClArC framework.
ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations.
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 prediction strategies can rarely be understood.
Explanation-Guided Training for Cross-Domain Few-Shot Classification
It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models.
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI.
Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models
We develop variants of layer-wise relevance propagation (LRP) and gradient-based explanation methods, tailored to image captioning models with attention mechanisms.
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Based on a recent technique - Spectral Relevance Analysis - we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit CH behavior, (b) several approaches denoted as Class Artifact Compensation (ClArC), which are able to effectively and significantly reduce a model's CH behavior.
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector.
Towards Best Practice in Explaining Neural Network Decisions with LRP
In this paper, we focus on a popular and widely used method of XAI, the Layer-wise Relevance Propagation (LRP).
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Deep learning has recently gained popularity in digital pathology due to its high prediction quality.
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior.
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems.
iNNvestigate neural networks!
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions.
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images.
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation
Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application.
