https://github.com/00why00/glocal
공식 코드Glocal Energy-based Learning for Few-Shot Open-Set Recognition
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Open Set Learning
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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.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Anjin-Liu/Openset_Learning_AOSR
공식 코드https://github.com/ArminLee/OWOD_Review
공식 코드https://github.com/Cogito2012/DEAR
공식 코드https://github.com/MrtnMndt/OCDVAEContinualLearning
공식 코드Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
https://github.com/MrtnMndt/OCDVAE_ContinualLearning
공식 코드Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
https://github.com/Vill-Lab/2023-TIFS-DTIBA
공식 코드Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
https://github.com/Xiaziheng89/Adversarial-Motorial-Prototype-Framework-for-Open-Set-Recognition
공식 코드Adversarial Motorial Prototype Framework for Open Set Recognition
https://github.com/Xiaziheng89/Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition
공식 코드Spatial Location Constraint Prototype Loss for Open Set Recognition
https://github.com/acse-dc1823/spotID
공식 코드Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
https://github.com/agaldran/t3po
공식 코드Test Time Transform Prediction for Open Set Histopathological Image Recognition
https://github.com/aghdamamir/actalign
공식 코드ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
https://github.com/aimerykong/OpenGAN
공식 코드https://github.com/aiml-ifi/openset-imagenet-comparison
공식 코드Large-Scale Evaluation of Open-Set Image Classification Techniques
https://github.com/albert0147/onering
공식 코드OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
https://github.com/aleflabo/COSKAD
공식 코드Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
https://github.com/alshami52/COOOL_benchmark
공식 코드COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving
https://github.com/amir-aghdam/ActAlign
공식 코드ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
https://github.com/antoalli/3d_os
공식 코드https://github.com/bardisafa/open-set-atr
공식 코드https://github.com/boschresearch/multiosr
공식 코드https://github.com/chuanxinggeng/ossl
공식 코드Dynamic Against Dynamic: An Open-set Self-learning Framework
https://github.com/cwdao/paper_code_robustness_enhanced_myoelectric_control
공식 코드Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition
https://github.com/daintlab/unknown-detection-benchmarks
공식 코드A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
https://github.com/daniyanaj/openltr
공식 코드Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasets
https://github.com/debabratapal7/dafosnet
공식 코드https://github.com/dimitymiller/cac-openset
공식 코드Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
https://github.com/dimitymiller/openset_vlms
공식 코드https://github.com/ebennequin/few-shot-open-set
공식 코드https://github.com/eccv22-ood-workshop/eccv22-ood-workshop.github.io
공식 코드Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
https://github.com/ema-marconato/glancenet
공식 코드https://github.com/feiyang-cai/osr_vit
공식 코드Open Set Recognition using Vision Transformer with an Additional Detection Head
https://github.com/fulin-gao/opengcd
공식 코드OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
https://github.com/gawainxu/openincremen
공식 코드OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
https://github.com/google/uncertainty-baselines
공식 코드Plex: Towards Reliability using Pretrained Large Model Extensions
https://github.com/guglielmocamporese/cvaecaposr
공식 코드Conditional Variational Capsule Network for Open Set Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.