Browse SoTA > Computer Vision > Out-of-Distribution Detection

Out-of-Distribution Detection

47 papers with code · Computer Vision

Detect out-of-distribution or anomalous examples.

Benchmarks

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.

OUT-OF-DISTRIBUTION DETECTION

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

22 Dec 2019OATML/bdl-benchmarks

From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit' their uncertainty to the dataset---when evaluated on our benchmark these underperform in comparison to simpler baselines.

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.

Ranked #2 on Out-of-Distribution Detection on CIFAR-100 (using extra training data)

ANOMALY DETECTION OUT-OF-DISTRIBUTION DETECTION

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

NeurIPS 2020 VLL-HD/FrEIA

In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.

OUT-OF-DISTRIBUTION DETECTION

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

Flows for simultaneous manifold learning and density estimation

NeurIPS 2020 johannbrehmer/manifold-flow

We introduce manifold-learning flows (M-flows), 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

Energy-based Out-of-distribution Detection

NeurIPS 2020 wetliu/energy_ood

We propose a unified framework for OOD detection that uses an energy score.

OUT-OF-DISTRIBUTION DETECTION

A Benchmark of Medical Out of Distribution Detection

8 Jul 2020mlmed/dl-web-xray

However it is unclear which OoDD method should be used in practice.

MEDICAL DIAGNOSIS OUT-OF-DISTRIBUTION DETECTION