Papers with Code 아카이브
Open Set Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hugo-oliveira/openseg
공식 코드https://github.com/iCGY96/ARPL
공식 코드Adversarial Reciprocal Points Learning for Open Set Recognition
https://github.com/icgy96/dnpg
공식 코드https://github.com/j-cb/ninco
공식 코드In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
https://github.com/jeffkinnison/shadho
공식 코드https://github.com/jianzongwu/awesome-open-vocabulary
공식 코드https://github.com/jinfengx/sasep
공식 코드https://github.com/jingkang50/oodsurvey
공식 코드https://github.com/jingkang50/openood
공식 코드https://github.com/jkomorniczak/owdsg
공식 코드Synthetic Non-stationary Data Streams for Recognition of the Unknown
https://github.com/jmandivarapu1/Deep-Active-Learning-via-Open-Set-Recognition
공식 코드https://github.com/jun-cen/unified_open_set_recognition
공식 코드The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition
https://github.com/junzhuang-code/NEGMGAN
공식 코드Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks
https://github.com/kpeng9510/hyprometa
공식 코드Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization
https://github.com/kpeng9510/os-sar
공식 코드Navigating Open Set Scenarios for Skeleton-based Action Recognition
https://github.com/lab176344/Traffic_Sceanrios-VoteBasedEVT
공식 코드https://github.com/layumi/A_reID
공식 코드Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
https://github.com/layumi/Oxford-Paris-Attack
공식 코드Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
https://github.com/lhrst/ase
공식 코드Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
https://github.com/lts5/distill-soda
공식 코드https://github.com/lts5/proto-sf-osda
공식 코드https://github.com/machine-listeners-valencia/fsl_osr_dataset_baseline
공식 코드https://github.com/marcosvendramini/GeMOS
공식 코드https://github.com/matejgrcic/DenseHybrid
공식 코드DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
https://github.com/matejgrcic/Vistas-NP
공식 코드Dense open-set recognition with synthetic outliers generated by Real NVP
https://github.com/mattochal/enhanced-outlier-logits
공식 코드EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
https://github.com/mohamad-m2/federated-learning
공식 코드FedOS: using open-set learning to stabilize training in federated learning
https://github.com/mrusci/ondevice-fewshot-kws
공식 코드Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
https://github.com/okankop/Driver-Anomaly-Detection
공식 코드Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
https://github.com/rafaelvareto/maximal-entropy-loss
공식 코드Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
https://github.com/raziehkaviani/rdosr
공식 코드Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
https://github.com/rshaojimmy/ECCV2020-OSAD
공식 코드https://github.com/saif-mahmud/hierarchical-attention-HAR
공식 코드Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
https://github.com/scottjingtt/srosda
공식 코드Towards Novel Target Discovery Through Open-Set Domain Adaptation
https://github.com/sgvaze/osr_closed_set_all_you_need
공식 코드Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.