Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts
Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable, but they often struggle in data-scarce settings where insufficient training samp…
Explainable ModelsFew-Shot Image Classificationimage-classificationImage ClassificationExplainability and Continual Learning meet Federated Learning at the Network Edge
As edge devices become more capable and pervasive in wireless networks, there is growing interest in leveraging their collective compute power for distributed learning. However, optimizing learning at the network edge en…
Continual LearningEdge-computingExplainable ModelsFederated Learning+2Explainable post-training bias mitigation with distribution-based fairness metrics
We develop a novel optimization framework with distribution-based fairness constraints for efficiently producing demographically blind, explainable models across a wide range of fairness levels. This is accomplished thro…
Explainable ModelsFairnessBioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explaina…
Explainable ModelsGraph Neural NetworkExDDV: A New Dataset for Explainable Deepfake Detection in Video
The ever growing realism and quality of generated videos makes it increasingly harder for humans to spot deepfake content, who need to rely more and more on automatic deepfake detectors. However, deepfake detectors are a…
DeepFake DetectionExplainable ModelsFace SwappingIn-Context Learning+1Tell me why: Visual foundation models as self-explainable classifiers
Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In this sense, self-explainable models (SEM…
Explainable ModelsForecasting the future development in quality and value of professional football players for applications in team management
Transfers in professional football (soccer) are risky investments because of the large transfer fees and high risks involved. Although data-driven models can be used to improve transfer decisions, existing models focus o…
Explainable ModelsManagementUncertainty QuantificationExplainability for Vision Foundation Models: A Survey
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and…
Decision MakingExplainable ModelsSurveyPrompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or do…
Explainable Artificial Intelligence (XAI)Explainable ModelsFine-Grained Image ClassificationInterpretable Machine Learning+1Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids
The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules with targeted properties. However, the focus has often been on creating highly efficient predictive model…
Explainable ModelsExplainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks
Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cau…
ClassificationExplainable ModelsFault DetectionFault Diagnosis+3Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging …
ClassificationDiagnosticExplainable artificial intelligenceExplainable Models+2Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical image analysis. These methods can provide …
DiagnosticExplainable ModelsMedical Image AnalysisReproducibility study of "LICO: Explainable Models with Language-Image Consistency"
The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method…
Explainable Modelsimage-classificationImage ClassificationLanguage Modeling+1Explainable Diagnosis Prediction through Neuro-Symbolic Integration
Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have …
Diabetes PredictionDiagnosticExplainable ModelsPredictionCaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely ma…
Explainable ModelsThe Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations
Visual counterfactual explanation (CF) methods modify image concepts, e.g, shape, to change a prediction to a predefined outcome while closely resembling the original query image. Unlike self-explainable models (SEMs) an…
ClassificationcounterfactualCounterfactual ExplanationExplainable Artificial Intelligence (XAI)+2HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection
Stereotypes are generalised assumptions about societal groups, and even state-of-the-art LLMs using in-context learning struggle to identify them accurately. Due to the subjective nature of stereotypes, where what consti…
Explainable ModelsIn-Context LearningDensity Adaptive Attention-based Speech Network: Enhancing Feature Understanding for Mental Health Disorders
Speech-based depression detection poses significant challenges for automated detection due to its unique manifestation across individuals and data scarcity. Addressing these challenges, we introduce DAAMAudioCNNLSTM and …
Depression DetectionDiagnosticExplainable ModelsChemistry-informed Machine Learning Explains Calcium-binding Proteins Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions
Proteins' fuzziness are features for communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter…
Explainable Models