Out-of-Distribution Detection
Leaderboards
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Greatest papers with code
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
ICCV 2019
• clovaai/CutMix-PyTorch
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Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
#6 best model for
Image Classification
on CIFAR-100
IMAGE CAPTIONING IMAGE CLASSIFICATION OBJECT LOCALIZATION OUT-OF-DISTRIBUTION DETECTION
Learning Confidence for Out-of-Distribution Detection in Neural Networks
• uoguelph-mlrg/confidence_estimation
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Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.
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).
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
NeurIPS 2019
• hendrycks/ss-ood
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Self-supervision provides effective representations for downstream tasks without requiring labels.
ANOMALY DETECTION OUTLIER DETECTION 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.
A Benchmark for Anomaly Segmentation
These novel baselines along with our anomaly segmentation benchmark open the door to further research in large-scale out-of-distribution detection and segmentation.
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category.
Metric Learning for Novelty and Anomaly Detection
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.
ANOMALY DETECTION METRIC LEARNING OUT-OF-DISTRIBUTION DETECTION TRAFFIC SIGN RECOGNITION
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
• MrtnMndt/Deep_Openset_Recognition_through_Uncertainty
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We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition.
Inhibited Softmax for Uncertainty Estimation in Neural Networks
We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output.