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

shftan/tree_ensemble_distance 22 Nov 2016

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 CVPR 2022

Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved.

The Approximation of the Dissimilarity Projection

emanuele/prni2012_dissimilarity 2 Apr 2015

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.

ProLFA: Representative Prototype Selection for Local Feature Aggregation

indussky8/demo_ProLFA 24 Oct 2019

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

bghojogh/Curvature-Anomaly-Detection 5 Apr 2020

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 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

Vrroom/vectorrvnn 23 Nov 2021

Selection functionality is as fundamental to vector graphics as it is for raster data.

Latent Distribution Adjusting for Face Anti-Spoofing

RicardooYoung/LatentDistributionAdjusting 16 May 2023

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.