Papers Prototype Selection
“Prototype Selection” 태그가 달린 논문 29편 · 필터 해제
This part looks alike this: identifying important parts of explained instances and prototypes
Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify t…
DiversityFeature ImportancePrototype SelectionHybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI
Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Conventional diagnostic methods, reliant on clinical e…
Active LearningDiagnosticPrototype SelectionHandling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Federated Continual Learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address no…
Continual LearningFederated LearningPrototype SelectionAddressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor
Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not on…
Continual LearningFederated LearningPrototype SelectionAccelerating prototype selection with spatial abstraction
The increasing digitalization in industry and society leads to a growing abundance of data available to be processed and exploited. However, the high volume of data requires considerable computational resources for apply…
Prototype SelectionPAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection
This paper describes PAWS-VMK, a prototypical deep learning approach that obtains state-of-the-art results for image classification tasks in both a semi-supervised learning (SSL) and out-of-distribution (OOD) detection c…
Benchmarkingimage-classificationImage ClassificationOut-of-Distribution Detection+2Latent Distribution Adjusting for Face Anti-Spoofing
With the development of deep learning, the field of face anti-spoofing (FAS) has witnessed great progress. FAS is usually considered a classification problem, where each class is assumed to contain a single cluster optim…
Face Anti-SpoofingPrototype SelectionDualRel: Semi-Supervised Mitochondria Segmentation From a Prototype Perspective
Automatic mitochondria segmentation enjoys great popularity with the development of deep learning. However, existing methods rely heavily on the labor-intensive manual gathering by experienced domain experts. And nai…
Prototype SelectionSegmentationProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits
In this work, we propose a multi-armed bandit-based framework for identifying a compact set of informative data instances (i.e., the prototypes) from a source dataset $S$ that best represents a given target set $T$. Prot…
Decision MakingMulti-Armed BanditsPrototype SelectionSelf-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be ac…
class-incremental learningClass Incremental LearningIncremental LearningPrototype SelectionReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives
Selection functionality is as fundamental to vector graphics as it is for raster data. But vector selection is quite different: instead of pixel-level labeling, we make a binary decision to include or exclude each vector…
Prototype SelectionVector GraphicsSPOT: A framework for selection of prototypes using optimal transport
In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset. Summarizing a given target dataset via representative examples is…
Decision MakingPrototype SelectionLearning Sparse Prototypes for Text Generation
Prototype-driven text generation uses non-parametric models that first choose from a library of sentence "prototypes" and then modify the prototype to generate the output text. While effective, these methods are ineffici…
Language ModelingLanguage ModellingPrototype SelectionRetrieval+3Anomaly Detection and Prototype Selection Using Polyhedron Curvature
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for a point, we consider every data point as…
Anomaly DetectionDenoisingImage DenoisingOut-of-Distribution Detection+1Modeling User Behaviors in Machine Operation Tasks for Adaptive Guidance
An adaptive guidance system that supports equipment operators requires a comprehensive model, which involves a variety of user behaviors that considers different skill and knowledge levels, as well as rapid-changing task…
Model SelectionPrototype SelectionDifferentially Private Distributed Data Summarization under Covariate Shift
We envision AI marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast number of data samples. One of the key chal…
Data SummarizationPrototype SelectionProLFA: Representative Prototype Selection for Local Feature Aggregation
Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. Existing feature aggregation (FA) app…
Computational EfficiencyPrototype SelectionImproving classification performance by feature space transformations and model selection
Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of mo…
ClassificationGeneral ClassificationModel SelectionPrototype SelectionLearning a sparse database for patch-based medical image segmentation
We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data. The proposed…
Image SegmentationMedical Image SegmentationPrototype SelectionSegmentation+2Ignorance-Aware Approaches and Algorithms for Prototype Selection in Machine Learning
Operating with ignorance is an important concern of the Machine Learning research, especially when the objective is to discover knowledge from the imperfect data. Data mining (driven by appropriate knowledge discovery to…
BIG-bench Machine LearningPrototype Selection