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Out-of-Distribution Detection

18 papers with code · Computer Vision

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Learning Confidence for Out-of-Distribution Detection in Neural Networks

13 Feb 2018uoguelph-mlrg/confidence_estimation

Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong.

OUT-OF-DISTRIBUTION DETECTION

Your classifier is secretly an energy based model and you should treat it like one

ICLR 2020 wgrathwohl/JEM

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x, y).

CALIBRATION OUT-OF-DISTRIBUTION DETECTION

Using Pre-Training Can Improve Model Robustness and Uncertainty

28 Jan 2019hendrycks/pre-training

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.

CALIBRATION OUT-OF-DISTRIBUTION DETECTION

A Benchmark for Anomaly Segmentation

25 Nov 2019hendrycks/anomaly-seg

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 SEMANTIC SEGMENTATION

Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition

CVPR 2019 naraysa/gzsl-od

We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category.

ACTION RECOGNITION IN VIDEOS OUT-OF-DISTRIBUTION DETECTION

Metric Learning for Novelty and Anomaly Detection

16 Aug 2018mmasana/OoD_Mining

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?

26 Aug 2019MrtnMndt/Deep_Openset_Recognition_through_Uncertainty

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.

OPEN SET LEARNING OUT-OF-DISTRIBUTION DETECTION

Inhibited Softmax for Uncertainty Estimation in Neural Networks

3 Oct 2018MSusik/Inhibited-softmax

We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output.

OUT-OF-DISTRIBUTION DETECTION SENTIMENT ANALYSIS