Out-of-Distribution Detection
Detect out-of-distribution or anomalous examples.
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Likelihood Ratios for Out-of-Distribution Detection
NeurIPS 2019
• google-research/google-research
• 
We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
Likelihood Ratios for Out-of-Distribution Detection
NeurIPS 2019
• google-research/google-research
•
We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
ICCV 2019
• clovaai/CutMix-PyTorch
• 
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
SOTA for Image Captioning on COCO
DOMAIN GENERALIZATION IMAGE CAPTIONING IMAGE CLASSIFICATION OBJECT LOCALIZATION OUT-OF-DISTRIBUTION DETECTION
Deep Anomaly Detection with Outlier Exposure
ICLR 2019
• hendrycks/outlier-exposure
•
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
SOTA for Anomaly Detection on CIFAR-10 model detecting CIFAR-10 (using extra training data)
Your classifier is secretly an energy based model and you should treat it like one
We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x, y).
Learning Confidence for Out-of-Distribution Detection in Neural Networks
• uoguelph-mlrg/confidence_estimation
•
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
NeurIPS 2019
• hendrycks/ss-ood
•
Self-supervision provides effective representations for downstream tasks without requiring labels.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
NeurIPS 2019
• hendrycks/ss-ood
•
Self-supervision provides effective representations for downstream tasks without requiring labels.
#2 best model for
Anomaly Detection
on CIFAR-10 model detecting CIFAR-10
ANOMALY DETECTION OUTLIER DETECTION OUT-OF-DISTRIBUTION DETECTION SELF-SUPERVISED LEARNING
Flows for simultaneous manifold learning and density estimation
• johannbrehmer/manifold-flow
•
We introduce manifold-modeling flows (MFMFs), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
DENOISING DENSITY ESTIMATION DIMENSIONALITY REDUCTION OUT-OF-DISTRIBUTION DETECTION
Using Pre-Training Can Improve Model Robustness and Uncertainty
He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training.