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open-set classification

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

https://github.com/aiml-ifi/openset-imagenet

공식 코드

Large-Scale Open-Set Classification Protocols for ImageNet

https://github.com/aiml-ifi/openset-imagenet-comparison

공식 코드

Large-Scale Evaluation of Open-Set Image Classification Techniques

https://github.com/bardisafa/open-set-atr

공식 코드

Open-Set Automatic Target Recognition

https://github.com/gina9726/protect

공식 코드

ProTeCt: Prompt Tuning for Taxonomic Open Set Classification

https://github.com/lumia-group/lot-insts

공식 코드

Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset

https://github.com/nukezil/iomatch

공식 코드

IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization

https://github.com/rafaelvareto/maximal-entropy-loss

공식 코드

Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation

https://github.com/raziehkaviani/rdosr

공식 코드

Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery

https://github.com/sbdzdz/disco

공식 코드

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization

https://github.com/sbdzdz/idsprites

공식 코드

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization

https://github.com/xuyingzhongguo/mcx-api

공식 코드

Learning Pairwise Interaction for Generalizable DeepFake Detection

https://gitlab.idiap.ch/bob/bob.paper.neurips2024_model_pairing/

공식 코드

Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks

https://github.com/Recognito-Vision/Face-SDK-Linux-Demos

Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss

https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML

Unbiased Evaluation of Deep Metric Learning Algorithms

https://github.com/daniyanaj/openltr

IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization

https://github.com/saketd403/CROSR

Classification-Reconstruction Learning for Open-Set Recognition

https://github.com/sbadirli/Open-Set-Authorship-Attribution

Open Set Authorship Attribution toward Demystifying Victorian Periodicals

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