Prototype Selection
9 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Ensembles of decision trees perform well on many problems, but are not interpretable.
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
g-u-n/pycil
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CVPR 2022
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.
Latent Distribution Adjusting for Face Anti-Spoofing
In this work, we propose a unified framework called Latent Distribution Adjusting (LDA) with properties of latent, discriminative, adaptive, generic to improve the robustness of the FAS model by adjusting complex data distribution with multiple prototypes.
The Approximation of the Dissimilarity Projection
We investigate the degree of approximation of this projection under different prototype selection policies and prototype set sizes in order to characterise its use on tractography data.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
ProLFA: 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.
Anomaly 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.
Learning Sparse Prototypes for Text Generation
jxhe/sparse-text-prototype
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NeurIPS 2020
While effective, these methods are inefficient at test time as a result of needing to store and index the entire training corpus.
ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives
Selection functionality is as fundamental to vector graphics as it is for raster data.
