https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
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/TianshuXie/Cut-Thumbnail
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/VLL-HD/IB-INN
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
https://github.com/VLL-HD/exact_information_bottleneck
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
https://github.com/Westlake-AI/openmixup
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/YU1ut/Ensemble-of-Leave-out-Classifiers
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
https://github.com/adverml/advopenood
https://github.com/agiachris/STAP
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
https://github.com/aiml-ifi/openset-imagenet-comparison
https://github.com/airplane2230/keras_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/alaalab/webcp
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
https://github.com/albertkx/conal
https://github.com/antoinedemathelin/openood
https://github.com/benolmbrt/MedicOOD
https://github.com/biweidai/SINF
https://github.com/chrvt/denoising-normalizing-flow
https://github.com/code-implementation1/Code5/tree/main/MIMO-UNet
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/dabsdamoon/MNIST-Auxiliary-Decoder
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/deargen/MT-ENet
https://github.com/dhruvpatel108/GANPriors
https://github.com/dlmacedo/isotropic-maximization-loss-and-entropic-score
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
https://github.com/dongdong69/MixAugmentation
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/drumpt/RotNet-OOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/dsgiitr/ML-Reproducibility-2020-DUQ
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
https://github.com/dvlab-research/mood
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
https://github.com/ensta-u2is/torch-uncertainty
https://github.com/ericjang/odin
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
https://github.com/feedbackward/addro
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
https://github.com/flxai/soft-brownian-offset
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
https://github.com/gaozhitong/atta
Scaling Out-of-Distribution Detection for Real-World Settings
https://github.com/giannifranchi/LP_BNN
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
https://github.com/gicheonkang/prograsp
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
https://github.com/glhr/ood-labelnoise
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
https://github.com/google-research/google-research/tree/master/genomics_ood
https://github.com/google-research/heldout-influence-estimation
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
https://github.com/haiv-lab/ospcoop_imagenet-bg
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.