Out-of-Distribution Detection
326 papers with code • 50 benchmarks • 22 datasets
Detect out-of-distribution or anomalous examples.
Benchmarks
These leaderboards are used to track progress in Out-of-Distribution Detection
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Libraries
Use these libraries to find Out-of-Distribution Detection models and implementationsDatasets
CIFAR-10
CIFAR-100
Fashion-MNIST
SST
STL-10
iSUN
Places365
20 Newsgroups
ImageNet-1k vs Textures
ImageNet-1k vs iNaturalist
Latest papers
Variational Bayesian Last Layers
We introduce a deterministic variational formulation for training Bayesian last layer neural networks.
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection
In this paper, we study the problem of out-of-distribution (OOD) detection in RL, which focuses on identifying situations at test time that RL agents have not encountered in their training environments.
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications.
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate.
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
bmucsanyi/bud
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Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification.
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
We introduce a formal statistical definition for the problem of backdoor detection in machine learning systems and use it to analyze the feasibility of such problems, providing evidence for the utility and applicability of our definition.
PUAD: Frustratingly Simple Method for Robust Anomaly Detection
However, we argue that logical anomalies, such as the wrong number of objects, can not be well-represented by the spatial feature maps and require an alternative approach.
How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data.
Learning with Mixture of Prototypes for Out-of-Distribution Detection
jeff024/palm
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To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
Feature shaping refers to a family of methods that exhibit state-of-the-art performance for out-of-distribution (OOD) detection.
