https://github.com/0xfafa/fa
공식 코드FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Papers with Code 아카이브
Out of Distribution (OOD) Detection
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Out of Distribution (OOD) Detection is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, as the model has not encountered it during training.
OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AlexMeinke/Provable-OOD-Detection
공식 코드Provably Robust Detection of Out-of-distribution Data (almost) for free
https://github.com/AlonZolfi/YolOOD
공식 코드YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
https://github.com/AngeloFang/MIPR_UAAN
공식 코드Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection
https://github.com/BaoshunWq/ood_GaCoOp
공식 코드Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context Optimization
https://github.com/ComputationalRadiationPhysics/InFlow
공식 코드InFlow: Robust outlier detection utilizing Normalizing Flows
https://github.com/DLR-RM/GRACE
공식 코드Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
공식 코드https://github.com/GT-RIPL/DistillMatch-SSCL
공식 코드Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
https://github.com/HHU-MMBS/plp-official-tmlr2024
공식 코드Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
https://github.com/JakobHavtorn/hvae-oodd
공식 코드https://github.com/Jarvisgivemeasuit/tagood
공식 코드https://github.com/Jingkang50/ICCV21_SCOOD
공식 코드https://github.com/Kaleidophon/know-your-limits
공식 코드Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning
공식 코드Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
https://github.com/Makbari1997/VAE-KPCA-HDBSCAN
공식 코드A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery
https://github.com/OATML/oatomobile
공식 코드Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
https://github.com/PolinaKirichenko/flows_ood
공식 코드Why Normalizing Flows Fail to Detect Out-of-Distribution Data
https://github.com/Puning97/AUTO-for-OOD-detection
공식 코드AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection
https://github.com/RAIVNLab/LLC
공식 코드LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
https://github.com/RanchoGoose/PViT
공식 코드PViT: Prior-augmented Vision Transformer for Out-of-distribution Detection
https://github.com/SLAD-ml/few-shot-ood
공식 코드Out-of-Domain Detection for Low-Resource Text Classification Tasks
https://github.com/SamWilso/HDFF_Official
공식 코드Hyperdimensional Feature Fusion for Out-Of-Distribution Detection
https://github.com/ShannonAI/kfolden-ood-detection
공식 코드$k$Folden: $k$-Fold Ensemble for Out-Of-Distribution Detection
https://github.com/ShimSoonYong/ZClassifier
공식 코드ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space
https://github.com/TaeseongYoon/DAEDL
공식 코드Uncertainty Estimation by Density Aware Evidential Deep Learning
https://github.com/UmarKhalidcs/RODD
공식 코드RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
https://github.com/VectorInstitute/gram-ood-detection
공식 코드Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
https://github.com/X1AOX1A/SeTAR
공식 코드SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
https://github.com/XavierXiao/Likelihood-Regret
공식 코드Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
https://github.com/a-vzer/waveletflowpytorch
공식 코드Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
https://github.com/adaptivemotorcontrollab/aros
공식 코드Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
https://github.com/aitor-martinez-seras/ood_on_snns
공식 코드A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
https://github.com/alessandro-canevaro/KAN-OOD
공식 코드Advancing Out-of-Distribution Detection via Local Neuroplasticity
https://github.com/alinlab/MASKER
공식 코드MASKER: Masked Keyword Regularization for Reliable Text Classification
https://github.com/alsace08/ood-math-reasoning
공식 코드Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.