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.
Improves out-of-distribution detection in neural networks.
problem Detecting out-of-distribution examples in neural networks.
method Normalizing flows and residual flow architecture for expressive density modeling.
result Significantly improved true negative rate (77.5%) compared to state-of-the-art (56.7%).
Deep learning models are known to be overconfident in their predictions on out of distribution inputs. This is a challenge when a model is trained on a particular input dataset, but receives out of sample data when deployed in practice. Recently, there has been work on building classifiers that are robust to out of dis…
Deep learning models are known to be overconfident in their predictions on out of distribution inputs. There have been several pieces of work to address this issue, including a number of approaches for building Bayesian neural networks, as well as closely related work on detection of out of distribution samples. Recent…
The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their…
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
problem Improving out-of-distribution coverage in multiclass image classification.
method Combining Bayesian deep learning with split conformal prediction methods.
result Combining methods can reduce out-of-distribution coverage in some cases.
Paper fine-tunes a simulation-driven estimator to reduce out-of-distribution errors.
problem Out-of-distribution errors in simulation-driven parameter estimators.
method Fine-tuning a Two-Stage estimator to improve accuracy for true parameters outside the sampled range.
result The fine-tuning approach reduces out-of-distribution errors and improves accuracy.
Study compares data-driven vs model-based MRS quantification strategies, focusing on resilience to out-of-distribution effects.
problem Resilience to out-of-distribution effects in data-driven MRS quantification.
method Compared three data-driven strategies (supervised regression, self-supervised learning, test-time adaptation) against model-based fitting tools.
result Test-time adaptation proved most resilient to out-of-distribution effects, while self-supervised learning achieved intermediate performance.
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…
GCRL learns causal factors for motion forecasting, improving out-of-distribution prediction.
problem Sensitivity to out-of-distribution data in conventional supervised learning methods.
method Generative Causal Representation Learning (GCRL) leveraging causality for knowledge transfer.
result Significantly outperforms prior models on out-of-distribution prediction.
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.
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.
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.
DRGO improves graph recommendation by mitigating noisy samples and enhancing out-of-distribution generalization.
problem Noisy samples in training data diminish recommendation systems' out-of-distribution generalization.
method DRGO uses a diffusion paradigm to reduce noisy effects and entropy regularization to avoid extreme weights.
result DRGO outperforms current methods in OOD recommendation across various distribution shifts.
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.
Bayesian autoencoder detects AI safety risk of out-of-distribution inputs.
problem Detecting unreliable predictions from AI models with different distributions.
method Probabilistic, unsupervised Bayesian variational autoencoder with posterior estimation.
result Effective detection of out-of-distribution inputs in both input and latent spaces.
Modern deep artificial neural networks have achieved great success in the domain of computer vision and beyond. However, their application to many real-world tasks is undermined by certain limitations, such as overconfident uncertainty estimates on out-of-distribution data or performance deterioration under data distri…
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.
In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves state-of-the-art uncertainty estimates both for in and out-of-distribution samples. Our contributions include a) demonstrating and adapting to …
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.
New method estimates treatment effects across different populations.
problem Estimating treatment effects across populations with changing distributions.
method SBRL-HAP framework combining balancing and independence regularizers with hierarchical attention.
result Significant improvement in HTE estimation across out-of-distribution populations.
New method uses KL divergence to detect out-of-distribution data effectively.
problem Flow-based models assign higher likelihoods to OOD data than ID data, making OOD detection challenging.
method Proposes a method leveraging KL divergence and local pixel dependence of representations for anomaly detection.
result Demonstrates effectiveness and robustness on prevalent benchmarks.
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.
New method finds unbiased subnetworks in biased models for better OOD performance.
problem How to improve out-of-distribution generalization in deep models.
method Functional modular probing method and Modular Risk Minimization.
result Even in biased models, there are unbiased subnetworks that can achieve better OOD performance.
Proposes DCV-ROOD framework for robust OOD detection evaluation.
problem Ensuring reliable OOD detection methods under diverse conditions.
method Dual Cross-Validation (DCV) adapted for OOD detection evaluation.
result Demonstrates fast convergence to true performance of OOD detection methods.
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.
Resultant improves likelihood-based U-OOD detection across various tasks.
problem Improving likelihood-based U-OOD detection performance.
method Resultant combines post-hoc prior and dataset entropy-mutual calibration techniques.
result Resultant achieves new state-of-the-art U-OOD detection performance.
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.
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.
Bayesian deep learning improves out-of-distribution detection but not always.
problem Improving the reliability of deep learning models in uncertain or novel data.
method Validation of likelihood-based Bayesian models for out-of-distribution detection.
result Bayesian deep learning models can marginally outperform conventional neural networks in certain conditions.
This paper improves model generalization by integrating diverse pretrained models.
problem Leveraging diverse pretrained models for robust out-of-distribution generalization.
method Characterize and integrate diverse pretrained models based on diversity and correlation shifts.
result Demonstrates state-of-the-art out-of-distribution generalization performance.
A pretrained LLM and its finetuned version can detect OOD data effectively.
problem Detecting out-of-distribution data in language models.
method Using the likelihood ratio between a pretrained and finetuned LLM.
result The likelihood ratio is an effective criterion for OOD detection.
A new principle for extrapolating regression outside training data.
problem Regression extrapolation when predictions are outside training data range.
method Data-adaptive marginal transformation and simple relationship assumption.
result Progression method offers guarantees on approximation error beyond training data range.
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.
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.
EntProp increases entropy of clean samples to generate out-of-distribution data for better DNN performance.
problem Improving deep neural networks' accuracy and robustness to out-of-distribution data.
method High entropy propagation using data augmentation and free adversarial training.
result EntProp achieves higher standard accuracy and robustness with lower training cost.
Paper tackles SCOD problem with optimal strategy and empirical validation.
problem Designing reliable prediction models abstaining from uncertain predictions.
method Bayes classifier for ID data and stochastic linear selector in 2D space.
result POSCOD method outperforms existing OOD methods.
SAL framework uses unlabeled data to improve OOD detection.
problem Lack of clean OOD samples makes OOD detection challenging.
method SAL framework separates candidate outliers and trains an OOD classifier.
result SAL achieves state-of-the-art performance on benchmarks.
Paper uses VAEs to detect radar targets in complex noise.
problem Detecting radar targets in compound clutter and thermal noise.
method Proposes a VAE architecture to distinguish radar targets from various noise types.
result The VAE outperforms classical detectors in challenging noise conditions.
Proposes a three-stage debiasing framework to improve out-of-distribution accuracy.
problem Inaccurate uncertainty estimations in bias-only models damage ensemble-based debiasing performance.
method Calibrates the bias-only model to improve its uncertainty estimations, creating a three-stage ensemble-based debiasing framework.
result The three-stage debiasing framework consistently outperforms traditional methods in out-of-distribution accuracy.
DSDE improves OoD detection by estimating model library proportions.
problem Uncertainty quantification and balanced error rates in model selection for OoD detection.
method Inverted sequential p-value strategies, change-point detection, automatic hyperparameter selection.
result DSDE reduces FPR from 11.07% to 3.31% on CIFAR10.
A framework for detecting out-of-distribution data in RL using uncertainty-based classification.
problem Detecting out-of-distribution data in deep reinforcement learning systems.
method A one-class classification problem approach based on epistemic uncertainty reduction.
result The proposed UBOOD framework reliably detects out-of-distribution situations when combined with ensemble-based uncertainty estimators.
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.
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.
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…
CGRL improves graph neural networks' OOD generalization by blocking spurious correlations.
problem Graph Neural Networks struggle with out-of-distribution data due to learning spurious correlations.
method Formulates a causal graph, uses backdoor adjustment, and introduces a loss replacement strategy.
result Significantly improves OOD generalization of GNNs, stabilizing mutual information learning.
Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distrib…
A new OOD detector using an overlap index improves accuracy without high computational costs.
problem Effective OOD detection for machine learning models in open-world scenarios.
method Proposes an overlap index-based confidence score function for OOD detection.
result The proposed method achieves competitive accuracy with lower computational costs compared to state-of-the-art detectors.