Probability Density from Latent Diffusion Models for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2508.15737v1
• Date: Thu, 21 Aug 2025 17:27:35 GMT
• Title: Probability Density from Latent Diffusion Models for Out-of-Distribution Detection
• Authors: Joonas Järve, Karl Kaspar Haavel, Meelis Kull,
• Abstract summary: Safety remains the main bottleneck to deploying machine-learning systems.<n>In generative models, the most natural OOD score is the data likelihood.<n>We show that likelihood often fails in practice, raising doubts about its usefulness.
• Score: 1.6954767541769011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite rapid advances in AI, safety remains the main bottleneck to deploying machine-learning systems. A critical safety component is out-of-distribution detection: given an input, decide whether it comes from the same distribution as the training data. In generative models, the most natural OOD score is the data likelihood. Actually, under the assumption of uniformly distributed OOD data, the likelihood is even the optimal OOD detector, as we show in this work. However, earlier work reported that likelihood often fails in practice, raising doubts about its usefulness. We explore whether, in practice, the representation space also suffers from the inability to learn good density estimation for OOD detection, or if it is merely a problem of the pixel space typically used in generative models. To test this, we trained a Variational Diffusion Model not on images, but on the representation space of a pre-trained ResNet-18 to assess the performance of our likelihood-based detector in comparison to state-of-the-art methods from the OpenOOD suite.

Related papers

• Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations [16.317861186815364]
  Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning systems.<n>Likelihood-based deep generative models have historically faced criticism for their unsatisfactory performance in OOD detection.<n>We show that likelihood-based methods can still perform on par with state-of-the-art methods when applied in the representation space of pre-trained encoders.
  arXiv  Detail & Related papers  (2025-04-10T14:30:41Z)
• Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes [13.246251147975192]
  Deep neural networks (DNNs) are often constructed under the closed-world assumption.<n>OOD samples are not always available during the training phase in real-world applications.<n>We propose a Gaussian-process-based OOD detection method to establish a decision boundary based on InD data only.
  arXiv  Detail & Related papers  (2024-12-30T12:57:31Z)
• What If the Input is Expanded in OOD Detection? [77.37433624869857]
  Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. We introduce a novel perspective, i.e., employing different common corruptions on the input space.
  arXiv  Detail & Related papers  (2024-10-24T06:47:28Z)
• Exploiting Diffusion Prior for Out-of-Distribution Detection [11.11093497717038]
  Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. We present a novel approach for OOD detection that leverages the generative ability of diffusion models and the powerful feature extraction capabilities of CLIP.
  arXiv  Detail & Related papers  (2024-06-16T23:55:25Z)
• Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows [7.91363551513361]
  Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting. We present a fully unsupervised approach which requires no exposure to OOD data, avoiding researcher bias in OOD sample selection. This is a post-hoc method which can be applied to any pretrained model, and involves training a lightweight auxiliary normalizing flow model to perform the out-of-distribution detection via density thresholding.
  arXiv  Detail & Related papers  (2024-02-09T16:51:01Z)
• Model-free Test Time Adaptation for Out-Of-Distribution Detection [62.49795078366206]
  We propose a Non-Parametric Test Time textbfAdaptation framework for textbfDistribution textbfDetection (abbr) abbr utilizes online test samples for model adaptation during testing, enhancing adaptability to changing data distributions. We demonstrate the effectiveness of abbr through comprehensive experiments on multiple OOD detection benchmarks.
  arXiv  Detail & Related papers  (2023-11-28T02:00:47Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Using Semantic Information for Defining and Detecting OOD Inputs [3.9577682622066264]
  Out-of-distribution (OOD) detection has received some attention recently. We demonstrate that the current detectors inherit the biases in the training dataset. This can render the current OOD detectors impermeable to inputs lying outside the training distribution but with the same semantic information. We perform OOD detection on semantic information extracted from the training data of MNIST and COCO datasets.
  arXiv  Detail & Related papers  (2023-02-21T21:31:20Z)
• Free Lunch for Generating Effective Outlier Supervision [46.37464572099351]
  We propose an ultra-effective method to generate near-realistic outlier supervision. Our proposed textttBayesAug significantly reduces the false positive rate over 12.50% compared with the previous schemes.
  arXiv  Detail & Related papers  (2023-01-17T01:46:45Z)
• Provably Robust Detection of Out-of-distribution Data (almost) for free [124.14121487542613]
  Deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. In this paper we propose a novel method where from first principles we combine a certifiable OOD detector with a standard classifier into an OOD aware classifier. In this way we achieve the best of two worlds: certifiably adversarially robust OOD detection, even for OOD samples close to the in-distribution, without loss in prediction accuracy and close to state-of-the-art OOD detection performance for non-manipulated OOD data.
  arXiv  Detail & Related papers  (2021-06-08T11:40:49Z)
• Learn what you can't learn: Regularized Ensembles for Transductive Out-of-distribution Detection [76.39067237772286]
  We show that current out-of-distribution (OOD) detection algorithms for neural networks produce unsatisfactory results in a variety of OOD detection scenarios. This paper studies how such "hard" OOD scenarios can benefit from adjusting the detection method after observing a batch of the test data. We propose a novel method that uses an artificial labeling scheme for the test data and regularization to obtain ensembles of models that produce contradictory predictions only on the OOD samples in a test batch.
  arXiv  Detail & Related papers  (2020-12-10T16:55:13Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.