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Found 8 papers, 5 papers with code
Outlier detection by ensembling uncertainty with negative objectness
We therefore reconsider direct prediction of K+1 logits that correspond to K groundtruth classes and one outlier class.
Real time dense anomaly detection by learning on synthetic negative data
Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data.
Hybrid Open-set Segmentation with Synthetic Negative Data
Our score can be implemented by upgrading any closed-set segmentation model with dense estimates of dataset posterior and unnormalized data likelihood.
On Advantages of Mask-level Recognition for Outlier-aware Segmentation
Most dense recognition approaches bring a separate decision in each particular pixel.
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
We blend these two predictions into a hybrid anomaly score which allows dense open-set recognition on large natural images.
Ranked #3 on
Scene Segmentation
on StreetHazards
(using extra training data)
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
Standard machine learning is unable to accommodate inputs which do not belong to the training distribution.
Ranked #2 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
Densely connected normalizing flows
Normalizing flows are bijective mappings between inputs and latent representations with a fully factorized distribution.
Ranked #2 on
Image Generation
on ImageNet 32x32
(bpd metric)
Dense open-set recognition with synthetic outliers generated by Real NVP
We obtain the synthetic outliers by sampling an RNVP model which is jointly trained to generate datapoints at the border of the training distribution.
