https://github.com/herbps10/adaptiveconformal
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Papers with Code 아카이브
Out-of-Distribution Detection
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Detect out-of-distribution or anomalous examples.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hh-xiaohu/Image-augementation-pytorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/hongxin001/JoCoR
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hongxin001/logitnorm_ood
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hongxin001/open-sampling
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hy18284/mixdiff
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
https://github.com/hysts/pytorch_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/ildoonet/cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jis478/Tensorflow/tree/master/TF2.0/Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jis478/cutmix_tensorflow2
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/joelsimonoff/gram-adversary-detection
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
https://github.com/js-aguiar/wheat-object-detection
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/juergenlandauer/Maya-Challenge
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jun-cen/psl
https://github.com/jun-cen/unified_open_set_recognition
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
https://github.com/juricaruntas/meta-ood
https://github.com/kboseong/RotNet
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/kimjeyoung/Mahalanobis-BERT
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
https://github.com/kkirchheim/pytorch-ood
Learning Confidence for Out-of-Distribution Detection in Neural Networks
https://github.com/kornia/kornia
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/kschwethelm/hyperboliccv
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers
https://github.com/kundajelab/abstention
https://github.com/lightning-uq-box/lightning-uq-box
https://github.com/lingorx/hieraseg
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
https://github.com/litianliu/fdbd-ood
Extremely Simple Activation Shaping for Out-of-Distribution Detection
https://github.com/liuch37/image-processing
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/llnl/deltauq
https://github.com/matt-baugh/nnood
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
https://github.com/mlmed/dl-web-xray
https://github.com/munhouiani/ood-knn
https://github.com/oliverzhang42/ood_medical_images
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/olivesgatech/RoSE
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
https://github.com/omegafragger/DDU
https://github.com/open-mmlab/mmclassification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/paganpasta/onmixup
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.