https://github.com/mengyuanchen21/neurips2024-csp
공식 코드Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models
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/microsoft/KC
공식 코드Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
https://github.com/microsoft/geospatial-ood-detection
공식 코드Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
https://github.com/ml-jku/hopfield-boosting
공식 코드Energy-based Hopfield Boosting for Out-of-Distribution Detection
https://github.com/mona4399/featuremixing
공식 코드Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
https://github.com/moose-eccv22/moose_eccv2022
공식 코드Probing Contextual Diversity for Dense Out-of-Distribution Detection
https://github.com/mpagli/agree-to-disagree
공식 코드Agree to Disagree: Diversity through Disagreement for Better Transferability
https://github.com/mu-cai/frl
공식 코드Out-of-distribution Detection via Frequency-regularized Generative Models
https://github.com/name-is-what/sego
공식 코드Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
https://github.com/naver/relis
공식 코드Reliability in Semantic Segmentation: Are We on the Right Track?
https://github.com/nazim1021/OOD-detection-using-OECC
공식 코드Outlier Exposure with Confidence Control for Out-of-Distribution Detection
https://github.com/ndb796/multipleinputmixup
공식 코드Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
https://github.com/neoques/graph-posterior-network
공식 코드Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
https://github.com/nicholas-mei/ood_detection_snojoe
공식 코드Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint Energy
https://github.com/njustkmg/AAAI25-ITP
공식 코드ITP: Instance-Aware Test Pruning for Out-of-Distribution Detection
https://github.com/o4lc/greg-ood
공식 코드https://github.com/parZival27/supervised-contrastive-learning-for-out-of-domain-detection
공식 코드https://github.com/peymanmorteza/gem
공식 코드Provable Guarantees for Understanding Out-of-distribution Detection
https://github.com/princetonvisualai/imagenetood
공식 코드ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
https://github.com/pris-nlp/coling2022_bayesian-for-ood
공식 코드Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation
https://github.com/qingyangzhang/dul
공식 코드The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection
https://github.com/qinyu-allen-zhao/optfsood
공식 코드Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
https://github.com/qitianwu/graphood-gnnsafe
공식 코드Energy-based Out-of-Distribution Detection for Graph Neural Networks
https://github.com/qqwsad5/is-ood
공식 코드Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
https://github.com/rajatkoner08/oodformer
공식 코드https://github.com/remic-othr/openmibood
공식 코드OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection
https://github.com/rishabbala/contrastive_learning_for_object_detection
공식 코드https://github.com/robin-chan/meta-ood
공식 코드https://github.com/rohban-lab/atd
공식 코드https://github.com/ronmckay/uqgan
공식 코드https://github.com/rushilanirudh/amp
공식 코드https://github.com/rwl93/bnp4ood
공식 코드A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
https://github.com/safednn-group/nap-ood
공식 코드Detection of out-of-distribution samples using binary neuron activation patterns
https://github.com/samd770/generative-models-knowledge
공식 코드https://github.com/samwilso/safe_official
공식 코드SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
https://github.com/selflein/EBM-OOD-Detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.