https://github.com/pluskid/structural-regularity
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Papers with Code 아카이브
Out-of-Distribution Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/pytorch/vision
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/robertsi20/Conceptualizing-Uncertainty
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
https://github.com/rwightman/pytorch-image-models
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/sangHa0411/ImageNet
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/sangHa0411/VIT
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/sayakpaul/Generalized-ODIN-TF
https://github.com/shyhyawJou/AUTC-Metric-for-OOD-Detection
Beyond AUROC & co. for evaluating out-of-distribution detection performance
https://github.com/sleep3r/garrus
https://github.com/sooonwoo/RotNet-OOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/stanislavfort/exploring_the_limits_of_OOD_detection
https://github.com/stevenboys/moon
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
https://github.com/t9liverpool/OpenMix
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
https://github.com/taldatech/deep-latent-particles-pytorch
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
https://github.com/thuiar/textoir
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/thuiar/textoir-demo
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/tmlr-group/class_prior
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
https://github.com/tmlr-group/neglabel
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
https://github.com/toshi-k/kaggle-bengaliai-handwritten-grapheme-classification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/wangermeng2021/FastClassification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/weizheliu/cross-domain-crowd-counting
https://github.com/wolfecameron/GIST
https://github.com/xden2331/attentive_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/xixiliu95/gen
https://github.com/yenchanghsu/Generalized-ODIN
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
https://github.com/yfeng002/impvaeood
https://github.com/yongsukyee/uncertain_blackholemass
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
https://github.com/yyliu01/it2
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
https://github.com/zeroshot-ood/ood-detection
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
https://github.com/zjs975584714/she_ood_detection
https://github.com/zzzace2000/robust_cls_model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.