MADOD meta-learns invariant features for OOD detection across unseen domains.
problem Simultaneous covariate and semantic shifts in real-world machine learning applications.
method Meta-learning and G-invariance to learn robust, domain-invariant features.
result Superior performance in semantic OOD detection across unseen domains.
Paper proposes methods to help autonomous vehicles adapt to unexpected driving scenarios.
problem Autonomous vehicles struggle with unexpected driving conditions.
method Robust imitative planning (RIP) and adaptive robust imitative planning (AdaRIP) methods to detect and adapt to distribution shifts.
result Methods outperform current state-of-the-art approaches in nuScenes prediction challenge.
Meta-learning improves OoD detection with minimal in-distribution data.
problem Efficient OoD detection with limited in-distribution data.
method Meta-learning in latent space with Gaussian mixture models.
result Meta-learning enhances OoD detection 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.
Self-poisoning in adaptive OOD detectors is explained with a sharp threshold theory and certified calibration.
problem Self-poisoning in adaptive OOD detectors.
method Modeling bank impurity as a generalized Pólya urn, proving almost-sure convergence to a mean-field equilibrium.
result A certified admission gate removes the transition at every contamination rate, controlling false positives label-free.
Paper investigates learnability of OOD detection under various conditions.
problem Learnability of OOD detection under different scenarios.
method Investigates PAC learning theory, proves impossibility theorems, and provides necessary and sufficient conditions.
result Some conditions for learnability of OOD detection may not hold in practical scenarios.
Paper investigates OOD detection learnability under various conditions.
problem Learnability of OOD detection under diverse and unknown test data.
method PAC learning theory applied to OOD detection, proving impossibility theorems and necessary conditions.
result Some conditions for learnability hold in practical scenarios.
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.
The paper explores how model complexity affects OOD detection performance.
problem Ensuring reliability and safety of machine learning systems through OOD detection.
method Investigates the relationship between model capacity and OOD detection performance using empirical and theoretical analysis.
result The Double Descent phenomenon is observed in post-hoc OOD detection, indicating that overparameterization can enhance OOD detection.
New method improves OOD detection without sacrificing generalization.
problem Improving OOD detection models that also generalize well.
method Decouples uncertainty learning from Bayesian perspective.
result Achieves state-of-the-art OOD detection performance.
ATOM improves robust OOD detection by mining informative auxiliary examples.
problem Robust OOD detection in open-world settings is challenging due to adversarial inputs.
method ATOM combines adversarial training with outlier mining to improve robustness.
result ATOM achieves state-of-the-art performance in OOD detection, reducing FPR by up to 57.99%.
The paper exposes common misconceptions about OOD detection and proposes a new framework.
problem Density-based OOD detection fails in deep learning settings.
method Proposes the OOD proxy framework to unify likelihood-ratio-based methods.
result Likelihood ratio is a principled method for OOD detection.
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.
Gaussian process detects OOD data without needing OOD samples.
problem Overconfident predictions from DNNs on OOD data.
method Gaussian process for uncertainty quantification and decision boundary.
result Outperforms state-of-the-art methods in OOD 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.
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.
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.
Paper proposes detecting OOD examples using Gram matrices and in-distribution data.
problem Detecting OOD examples with confidence and without OOD data.
method Characterize activity patterns with Gram matrices and identify anomalies in values.
result High OOD detection rates achieved without OOD data.
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.
Unified framework for OOD detection and generalization using graph theory.
problem Challenges in out-of-distribution (OOD) generalization and detection in real-world machine learning models.
method Graph-theoretic framework to jointly tackle OOD generalization and detection.
result Empirical validation of theoretical underpinnings with competitive performance.
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since we observe that training data is often insufficient in machine learning applications. In this work, we propose an OOD-resistant Pr…
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.
Combining training and post-training methods improves OOD detection accuracy.
problem Deep networks struggle with OOD detection.
method Divided OOD detection methods into training and post-training, then combined them.
result State-of-the-art results in OOD detection achieved.
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.
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 method detects outliers and uncertain points in data.
problem Detecting outliers and uncertain points in data using Bayesian methods.
method Generative model of data curation for aleatoric uncertainty, combining with epistemic uncertainty and outlier exposure.
result Principled Bayesian approach outperforms methods using aleatoric or epistemic uncertainty alone.
INK scores improve OOD detection for classifiers.
problem Detecting out-of-distribution inputs for classification models.
method INK scores operate on constrained latent embeddings modeled as a mixture of hyperspherical embeddings, optimizing in modern neural networks.
result INK establishes a new state-of-the-art in OOD detection.
A new framework detects anomalous inputs to DNNs.
problem Detecting adversarial and out-of-distribution inputs in DNNs.
method Unsupervised meta-algorithm with configurable components based on statistical testing.
result The proposed method effectively detects adversarial and OOD inputs.
Geometrically, high-likelihood regions in DGMs are unlikely to generate OOD data.
problem The paradox of high-likelihood OOD detection in deep generative models.
method Local intrinsic dimension estimation to identify high-likelihood regions that do not generate OOD data.
result A method pairing likelihoods and LID estimates for reliable OOD detection.
STOOD-X detects out-of-distribution samples without distributional assumptions and provides explainable visualizations.
problem Challenges in OOD detection, including restrictive assumptions, scalability issues, and lack of interpretability.
method Two-stage methodology combining statistical nonparametric test and explainability enhancements.
result Achieves competitive performance in high-dimensional and complex settings, with explainability framework enabling human oversight.
NADS improves OoD detection accuracy by 57%.
problem Uncertainty in machine learning models when encountering out-of-distribution data.
method NADS searches for a distribution of architectures that perform well on a given task, optimizing a stochastic OoD detection objective.
result NADS achieves up to 57% improvement in accuracy over state-of-the-art methods.
Paper presents a novel framework for OOD learning with human feedback.
problem Multifaceted challenges of OOD generalization and detection.
method Selective human feedback for labeling a small number of informative samples from wild data distribution.
result Enhanced robustness and precision in handling OOD scenarios.
New method uses batch normalization to improve OoD detection.
problem Out-of-distribution samples are not reliably detected by generative models.
method Proposes exploiting in-batch dependencies for OoD detection.
result Empirical results show improved robustness for high-dimensional images.
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.
Medix uses the median to detect outliers from unlabeled data for robust OOD detection.
problem Challenges in using unlabeled data for OOD detection due to mixed InD and OOD samples.
method Introduces Medix, a framework using the median operation to identify outliers from unlabeled data.
result Empirical results show Medix outperforms existing methods in open-world settings.
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.
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.
Unified framework for OOD detection using class ratio estimation.
problem Density-based OOD detection is unreliable for OOD images.
method Unified framework that builds energy-based models and employs differing base distributions, directly estimating the density ratio through class ratio estimation.
result Competitive results on OOD image problems compared to recent work.
Bayesian Neural Networks improve OOD detection with limited data.
problem Limited training data hinders reliable OOD detection.
method Bayesian Neural Networks with expected logit vectors.
result Bayesian methods outperform deterministic methods in small data settings.
P-OCS detects OOD samples in a low-dimensional subspace, outperforming existing methods.
problem Efficient OOD detection for deep learning models in open-world environments.
method P-OCS operates in the orthogonal complement of the principal subspace, applying a single projected perturbation.
result P-OCS achieves state-of-the-art OOD detection with negligible computational cost and without requiring model retraining.
This paper combines existing OOD detection methods to improve overall performance.
problem Improving robustness of neural networks in safety-critical applications.
method Integrates four strategies for combining multiple OOD detection scores.
result Enhanced OOD detection through multi-dimensional evaluation metrics.
Paper proposes a new method to detect out-of-distribution samples.
problem Detecting out-of-distribution samples for neural network classifiers.
method Generates effective OOD samples using a manifold learning network and trains an additional classifier.
result Proposed method consistently performs better than existing classifiers on MNIST and Fashion-MNIST datasets.
Framework uses human feedback to safely set OOD detection thresholds, reducing false positives.
problem Challenges in setting OOD detection thresholds for safety-critical applications.
method Mathematically grounded framework leveraging expert feedback to dynamically update thresholds.
result Guaranteed to meet FPR constraint while minimizing human feedback, maintaining FPR at most 5%.
This work proposes using Conformal Prediction to improve OOD detection scores and vice versa.
problem Improper evaluation of OOD detection scores due to finite sample size.
method Defining new conformal AUROC and FRP@TPR95 metrics and using OOD scores as non-conformity scores.
result Improved evaluation metrics and better interpretation of OOD scores.
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.
Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algori…
Enhances OOD detection using latent diffusion for more robust and efficient training.
problem Improving reliability of machine learning models in real-world scenarios.
method Proposes Outlier-Aware Learning (OAL) framework that generates synthetic OOD data in latent space and uses MICL and KD modules.
result Demonstrates superior performance on benchmark datasets.
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.