Papers Counterfactual Explanation
“Counterfactual Explanation” 태그가 달린 논문 177편 · 필터 해제
Graph Style Transfer for Counterfactual Explainability
Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted outcome. This task becomes particularly challenging for graph data due to preserving str…
counterfactualCounterfactual ExplanationGraph ClassificationStyle Transfer+1COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Graph neural networks (GNNs) have been widely used in various domains such as social networks, molecular biology, or recommendation systems. Concurrently, different explanations methods of GNNs have arisen to complement …
Binary ClassificationcounterfactualCounterfactual ExplanationDrug Discovery+1Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using b…
AttributeComputational EfficiencyCounterfactual ExplanationData Augmentation+9Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited da…
Contrastive LearningcounterfactualCounterfactual ExplanationFew-Shot Object Detection+2Ranking Counterfactual Explanations
AI-driven outcomes can be challenging for end-users to understand. Explanations can address two key questions: "Why this outcome?" (factual) and "Why not another?" (counterfactual). While substantial efforts have been ma…
counterfactualCounterfactual ExplanationCounterfactual Explanations for Model Ensembles Using Entropic Risk Measures
Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense interest in high-stakes applications such a…
counterfactualCounterfactual ExplanationvalidAdvancing climate model interpretability: Feature attribution for Arctic melt anomalies
The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets are experiencing rapid surface melting a…
Anomaly DetectioncounterfactualCounterfactual ExplanationA Novel Multi-Objective Evolutionary Algorithm for Counterfactual Generation
Machine learning algorithms that learn black-box predictive models (which cannot be directly interpreted) are increasingly used to make predictions affecting the lives of people. It is important that users understand the…
counterfactualCounterfactual ExplanationLeveraging counterfactual concepts for debugging and improving CNN model performance
Counterfactual explanation methods have recently received significant attention for explaining CNN-based image classifiers due to their ability to provide easily understandable explanations that align more closely with h…
counterfactualCounterfactual ExplanationCounterfactual Reasoningimage-classification+1Faithful Counterfactual Visual Explanations (FCVE)
Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performa…
counterfactualCounterfactual ExplanationDecision MakingRobust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
In recent years, explainability in machine learning has gained importance. In this context, counterfactual explanation (CE), which is an explanation method that uses examples, has attracted attention. However, it has bee…
counterfactualCounterfactual ExplanationDecision MakingCounterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
The complexity of modern electro-mechanical systems require the development of sophisticated diagnostic methods like anomaly detection capable of detecting deviations. Conventional anomaly detection approaches like signa…
Anomaly DetectioncounterfactualCounterfactual ExplanationDecision Making+3Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition
Attribution-based explanation techniques capture key patterns to enhance visual interpretability. However, these patterns often lack the granularity needed for insight in fine-grained tasks, particularly in cases of …
counterfactualCounterfactual ExplanationAdopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations
Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes thr…
counterfactualCounterfactual ExplanationTime SeriesTime Series AnalysisMulti-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification
Deep Learning systems excel in complex tasks but often lack transparency, limiting their use in critical applications. Counterfactual explanations, a core tool within eXplainable Artificial Intelligence (XAI), offer insi…
counterfactualCounterfactual ExplanationExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)+2Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI
Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) require white-box access to models, (2) are v…
counterfactualCounterfactual ExplanationCounterfactual Explanations via Riemannian Latent Space Traversal
The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions. Counterfactual explanations form a powerful tool for providing actionable explanations to pra…
counterfactualCounterfactual ExplanationDecoderM-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive s…
ClassificationcounterfactualCounterfactual ExplanationTime Series+1A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
Counterfactual explanations can be used to interpret and debug text classifiers by producing minimally altered text inputs that change a classifier's output. In this work, we evaluate five methods for generating counterf…
counterfactualCounterfactual ExplanationvalidTowards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to o…
counterfactualCounterfactual ExplanationDecision Making