https://github.com/kirk86/RAIOOD
공식 코드On the Importance of Regularisation & Auxiliary Information in OOD 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/kobybibas/pnml_ood_detection
공식 코드Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
https://github.com/kostadinovshalon/oln-ssos
공식 코드Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
https://github.com/lancopku/avg-avg
공식 코드Holistic Sentence Embeddings for Better Out-of-Distribution Detection
https://github.com/lancopku/gnome
공식 코드https://github.com/lancopku/well-classified-examples-are-underestimated
공식 코드Well-classified Examples are Underestimated in Classification with Deep Neural Networks
https://github.com/larsdoorenbos/ncis
공식 코드Non-Linear Outlier Synthesis for Out-of-Distribution Detection
https://github.com/larsmaaloee/BIVA
공식 코드https://github.com/lawliet-zzl/ca
공식 코드https://github.com/lawliet-zzl/fig
공식 코드Revealing the Distributional Vulnerability of Discriminators by Implicit Generators
https://github.com/lblaoke/BTCP
공식 코드Identifying Incorrect Classifications with Balanced Uncertainty
https://github.com/lblaoke/tlc
공식 코드https://github.com/liam0949/llm-ood
공식 코드VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
https://github.com/lifelong-ml/shels
공식 코드SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries
https://github.com/lili0415/dpu-ood-detection
공식 코드DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
https://github.com/limchaos/likelihood-ood
공식 코드Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
https://github.com/linasnas/dexter
공식 코드https://github.com/litianliu/fdbd-ood
공식 코드https://github.com/litianliu/nci-ood
공식 코드Detecting Out-of-Distribution Through the Lens of Neural Collapse
https://github.com/liuyuan-wen/av-ood-gzsl
공식 코드Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General Framework
https://github.com/llnl/amp
공식 코드https://github.com/lmb-freiburg/diffusion-for-ood
공식 코드Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
https://github.com/loic-lb/ood_mil
공식 코드On the detection of Out-Of-Distribution samples in Multiple Instance Learning
https://github.com/lulor/ood-class-separation
공식 코드Large Class Separation is not what you need for Relational Reasoning-based OOD Detection
https://github.com/mala-lab/adaptod
공식 코드Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
https://github.com/mala-lab/negprompt
공식 코드Learning Transferable Negative Prompts for Out-of-Distribution Detection
https://github.com/mala-lab/opencil
공식 코드OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning
https://github.com/mapleleaf6/zode
공식 코드Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
https://github.com/marclafon/gallop
공식 코드GalLoP: Learning Global and Local Prompts for Vision-Language Models
https://github.com/marclafon/heatood
공식 코드Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
https://github.com/marksgraham/ddpm-ood
공식 코드Unsupervised 3D out-of-distribution detection with latent diffusion models
https://github.com/marksgraham/transformer-ood
공식 코드Transformer-based out-of-distribution detection for clinically safe segmentation
https://github.com/matanat/GRACE
공식 코드Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
https://github.com/maxnygma/icla
공식 코드Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
https://github.com/mdabbah/COOD_benchmarking
공식 코드A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
https://github.com/megvii-research/FSSD_OoD_Detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.