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Prototype Selection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Vrroom/vectorrvnn

공식 코드

ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives

https://github.com/bghojogh/Curvature-Anomaly-Detection

공식 코드

Anomaly Detection and Prototype Selection Using Polyhedron Curvature

https://github.com/emanuele/prni2012_dissimilarity

공식 코드

The Approximation of the Dissimilarity Projection

https://github.com/indussky8/demo_ProLFA

공식 코드

ProLFA: Representative Prototype Selection for Local Feature Aggregation

https://github.com/jkarolczak/important-parts-of-prototypes

공식 코드

This part looks alike this: identifying important parts of explained instances and prototypes

https://github.com/jxhe/sparse-text-prototype

공식 코드

Learning Sparse Prototypes for Text Generation

https://github.com/shftan/tree_ensemble_distance

공식 코드

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux

Latent Distribution Adjusting for Face Anti-Spoofing

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Latent Distribution Adjusting for Face Anti-Spoofing

https://github.com/RicardooYoung/LatentDistributionAdjusting

Latent Distribution Adjusting for Face Anti-Spoofing

https://github.com/g-u-n/pycil

Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning

https://github.com/gregoirepetit/fetril

Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning

https://github.com/loft-br/xgboost-survival-embeddings

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.