Simple methods combine statistical tests for out-of-distribution detection.
problem Detecting data points not following the training distribution.
method Combining classical parametric tests (Rao's score test) and a typicality test.
result Combining Fisher's method of test statistics improves out-of-distribution detection accuracy.
Method enhances anomaly detection using contrastive learning and out-of-distribution data.
problem Improving anomaly detection in datasets with limited out-of-distribution data.
method Proposes a contrastive learning method that incorporates out-of-distribution data to enhance anomaly detection performance.
result The method significantly improves anomaly detection performance, even with limited out-of-distribution data.
Bayesian Gaussian Processes layer detects out-of-distribution data in medical imaging.
problem Detecting out-of-distribution data in medical imaging tasks.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates enable superior out-of-distribution detection compared to previous methods.
Single deep model detects out-of-distribution data with single forward pass.
problem Detecting out-of-distribution data points in neural networks.
method Deterministic uncertainty quantification (DUQ) using gradient penalty for reliable detection.
result Single model outperforms or matches ensemble methods in out-of-distribution detection.
NECO detects out-of-distribution data using neural collapse properties.
problem Detecting out-of-distribution data in machine learning models.
method NECO leverages neural collapse geometric properties to identify OOD data.
result NECO achieves state-of-the-art results on OOD detection tasks.
New framework detects out-of-distribution data by considering intrinsic ID attributes in outliers.
problem Deploying reliable machine learning systems requires effective out-of-distribution detection.
method Structured multi-view-based out-of-distribution detection learning (MVOL) framework.
result MVOL effectively utilizes both auxiliary OOD datasets and wild datasets with noisy in-distribution data.
Igeood detects out-of-distribution samples using information geometry.
problem Out-of-distribution (OOD) detection in machine learning systems.
method Igeood uses the Fisher-Rao geodesic distance to detect OOD samples from any pre-trained neural network.
result Igeood outperforms state-of-the-art methods on various network architectures and datasets.
Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safel…
Normalizing flows fail to detect OOD data due to learning local pixel correlations.
problem Detecting out-of-distribution data in machine learning systems.
method Investigated why normalizing flows fail to distinguish between in- and out-of-distribution data, and modified flow architecture to improve OOD detection.
result Modifying flow architecture can improve OOD detection by biasing the flow towards learning semantic structure of the target data.
Study complex-valued VAEs for radar OOD detection.
problem Detecting out-of-distribution signals in complex radar environments.
method Proposed and compared several detection metrics for CVAE.
result CVAE-MSE and latent-based scores outperform ANMF-Tyler.
A simple method flags images as out-of-distribution based on their distance to nearest neighbors.
problem Detecting images not aligned with a trained model's in-distribution data.
method Flag images as OOD if their average distance to K nearest neighbors is large in the classifier's representation space.
result Simple methods can outperform more complex ones when considering learned representations.
New method improves robustness of OOD detection models.
problem Detecting out-of-distribution inputs is critical for deep learning models.
method Proposes ALOE algorithm for robust training with adversarially crafted examples.
result ALOE substantially improves robustness of OOD detection on CIFAR-10 and CIFAR-100 datasets.
Bayesian layer improves image segmentation and out-of-distribution detection.
problem Outlier detection in image segmentation.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates improve out-of-distribution detection.
Soft labeling impacts OOD detection in neural networks.
problem Impact of soft labeling on OOD detection in deep neural networks.
method Empirical analysis of how soft labeling affects OOD detection performance.
result Soft labeling can either improve or deteriorate OOD detection performance.
A new algorithm detects out-of-distribution samples by concentrating them in feature space.
problem Building safe AI systems requires effective out-of-distribution detection.
method The paper proposes a novel algorithm based on the observation that OoD samples concentrate in feature space.
result The algorithm achieves state-of-the-art performance on various OoD detection benchmarks.
A new method approximates pNML for faster out-of-distribution detection.
problem Detecting out-of-distribution examples efficiently.
method Influence functions approximation of pNML for neural networks.
result The approximation effectively detects out-of-distribution examples.
SLUG method detects bias and out-of-distribution content in generative models.
problem Generative models can underrepresent certain groups and fail on out-of-distribution data.
method SLUG: A new uncertainty quantification method for VAEs combining Laplace approximations and stochastic trace estimators.
result SLUG's UQ score correlates with bias and out-of-distribution content.
Paper proposes contrastive training to improve OOD detection without needing explicit OOD examples.
problem Improving reliable detection of out-of-distribution inputs for machine learning systems.
method Contrastive training approach that doesn't require explicit OOD examples, using CLP score.
result Contrastive training significantly improves OOD detection performance on benchmarks, especially in near OOD classes.
A new loss function improves neural networks' out-of-distribution detection without side effects.
problem Neural networks struggle with out-of-distribution detection due to SoftMax loss issues.
method Proposes IsoMax loss replacing SoftMax loss, maintaining high entropy and fast inferences.
result Significantly improves neural networks' out-of-distribution detection performance.
Improved OOD detection using label smoothing and k-NN density estimates.
problem Detecting out-of-distribution examples in classification models.
method Label smoothing and k-NN density estimate on intermediate activations.
result Label smoothing improves OOD detection performance, both theoretically and empirically.
Bayesian nonparametric models improve OOD detection, especially with complex covariance structures.
problem Improving out-of-distribution detection methods, especially in complex scenarios.
method Proposes Bayesian nonparametric mixture models with hierarchical priors that generalize the Mahalanobis distance score.
result Bayesian nonparametric methods outperform existing OOD methods, especially in complex scenarios.
Hierarchical VAEs detect out-of-distribution data by identifying low-level in-distribution features.
problem Out-of-distribution data often has in-distribution low-level features, leading to misleading likelihood estimates in deep generative models.
method Developed a fast, scalable, unsupervised likelihood-ratio score for out-of-distribution detection based on hierarchical variational autoencoders.
result Achieved state-of-the-art results on out-of-distribution detection across various data and model combinations.
Single model detects abnormal samples across diverse tasks.
problem Detecting abnormal samples in machine learning.
method Introduced Diffusion Paths (DiffPath) using a single unconditional diffusion model.
result Single model performs OOD detection across diverse tasks.
WOOD detects out-of-distribution samples using Wasserstein distance.
problem Detecting samples from different distributions in neural networks.
method WOOD defines a Wasserstein-distance-based score to evaluate dissimilarity and solves an optimization problem.
result WOOD consistently outperforms other OOD detection methods.
FOOD detects out-of-distribution samples quickly without needing OOD data.
problem Detecting out-of-distribution samples efficiently in neural networks.
method Extended DNN classifier with Gaussian layer and log likelihood ratio test.
result FOOD achieves state-of-the-art performance and is fast and applicable.
New method detects OOD samples using neural network trajectories.
problem Lack of comprehensive layer exploration in OOD detection.
method Functional data perspective, analyzing sample trajectories through multi-layer classifier.
result Empirically validated as effective compared to state-of-the-art methods.
New method detects uncertainty in neural networks for out-of-distribution detection.
problem Detecting out-of-distribution inputs to ensure model reliability.
method Predictive topological uncertainty (pTU) based on persistent homology.
result pTU provides a statistical framework for OOD detection.
Proposes Likelihood Regret for VAEs to improve OOD detection.
problem VAEs can assign high likelihoods to OOD samples, making traditional likelihood thresholds unreliable.
method Introduces Likelihood Regret, a new OOD score for VAEs.
result Empirical results show Likelihood Regret outperforms existing methods for VAEs.
PEOC uses policy entropy to detect untrained states in RL.
problem Detecting untrained states in reinforcement learning for safety.
method Policy entropy based one-class classifier.
result PEOC is highly competitive and reliable.
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertainty estimation techniques directly on the agent's value estimating neural network to detect OOD samples. The focus of our work lies in analyzi…
Deep Neural Networks are powerful models that attained remarkable results on a variety of tasks. These models are shown to be extremely efficient when training and test data are drawn from the same distribution. However, it is not clear how a network will act when it is fed with an out-of-distribution example. In this …
Proposes a framework for OOD detection combining multiple statistics.
problem Detecting out-of-distribution (OOD) samples reliably during inference.
method Multiple hypothesis testing with conformal p-values.
result Uniformly outperforms threshold-based tests across different datasets and neural networks.
When presented with Out-of-Distribution (OOD) examples, deep neural networks yield confident, incorrect predictions. Detecting OOD examples is challenging, and the potential risks are high. In this paper, we propose to detect OOD examples by identifying inconsistencies between activity patterns and class predicted. We …
The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of feature activations in the training data for adequately distinguishing abnormalities, and the state-of-the-art method uses Gaussian distrib…
Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world. Typical components such as deep neural networks, however, introduce new types of hazards that may impact system safety. The system behavior depends on data that are availa…
Paper aims to ensure reliable detection of out-of-distribution data with certifiable worst-case guarantees.
problem Deep neural networks are overconfident with OOD inputs, posing safety risks.
method Enforces low confidence and bounds in an l∞-ball around OOD points using interval bound propagation (IBP). result Certifiable worst-case guarantees for OOD detection are possible without significant loss in accuracy.
Bayesian neural networks fail at OOD detection, revealing fundamental issues.
problem The ability of Bayesian neural networks to detect out-of-distribution data.
method Empirical study of Bayesian inference in neural networks, including infinite-width and finite-width cases using Hamiltonian Monte Carlo.
result Bayesian inference in common neural network architectures does not lead to good OOD detection.
MPT improves CNN and energy-based models' OOD detection and generalization.
problem Challenging out-of-distribution detection in computer vision.
method Applying Maximum Probability Theorem as a regularization scheme in CNN and energy-based models.
result MPT-based regularization strategy stabilizes and improves generalization and robustness of base models.
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to OoD samples compared to ones from the training distribution, preventing simple threshold-based detection rules. We demonstrate that OoD det…
Improved VAE model enhances uncertainty estimation for out-of-distribution samples.
problem VAEs assign higher likelihood to out-of-distribution inputs.
method INCPVAE integrates noise contrastive prior into VAEs for reliable uncertainty estimation.
result INCPVAE outperforms standard VAEs in uncertainty estimation for OOD inputs.
MixDiff detects OOD samples in constrained access environments by comparing perturbed samples.
problem Detecting out-of-distribution samples in models with restricted access.
method Apply identical perturbation to target and similar ID sample, compare model outputs.
result MixDiff enhances OOD detection performance consistently across various datasets.
Bayesian neural networks fail at out-of-distribution detection, revealing fundamental issues.
problem Out-of-distribution detection with Bayesian neural networks.
method Study of Bayesian inference with function space priors and comparison to Gaussian processes.
result Bayesian inference with function space priors does not lead to good OOD detection.
We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separ…
New method detects out-of-distribution samples in regression tasks.
problem Detecting instances far from training data in regression models.
method Estimating predictor entropy based on nearest neighbors and generative models.
result A new method for robust OOD detection in regression tasks.
Deep neural networks are known to achieve superior results in classification tasks. However, it has been recently shown that they are incapable to detect examples that are generated by a distribution which is different than the one they have been trained on since they are making overconfident prediction for Out-Of-Dist…
Detects out-of-distribution sentences in Neural Machine Translation.
problem Identifying sentences from a different language than the training data.
method Developed a new uncertainty measure for long sequences of words in Transformers.
result Shows ability to identify Dutch sentences as German input.
OOD detection methods often misidentify OOD points, leading to ineffective safety improvements.
problem Improving model safety through OOD detection methods often leads to incorrect identification of out-of-distribution points.
method Re-examine popular OOD detection procedures based on predictive uncertainty or features of supervised models trained on in-distribution data.
result Popular OOD detection methods incorrectly conflate high uncertainty and far feature-space distance with being out-of-distribution.
KPCA improves OoD detection by separating InD and OoD data.
problem Insufficiency of PCA in detecting OoD data from InD data.
method Kernel PCA (KPCA) with task-specific kernels.
result KPCA achieves superior OoD detection performance.