FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2412.01596v1
• Date: Mon, 02 Dec 2024 15:15:24 GMT
• Title: FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
• Authors: Brian K. S. Isaac-Medina, Mauricio Che, Yona F. A. Gaus, Samet Akcay, Toby P. Breckon,
• Abstract summary: We study previously unexplored and inherent vulnerabilities within the free energy score formulation for Out-of-Distribution (OOD) detection.<n>We introduce novel regularisation to maximize the least singular value of the final linear layer, hence enhancing inter-sample free energy separation.<n>Our experiments show that FEVER-OOD techniques achieve state of the art OOD detection in Imagenet-100, with average OOD false positive rate (at 95% true positive rate) of 35.83%.
• Score: 12.139516247199179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distribution (OOD) examples, resulting in unpredictable behaviour for open-set environments. Recent works have demonstrated that the free energy score is an effective measure of uncertainty for OOD detection given its close relationship to the data distribution. However, despite free energy-based methods representing a significant empirical advance in OOD detection, our theoretical analysis reveals previously unexplored and inherent vulnerabilities within the free energy score formulation such that in-distribution and OOD instances can have distinct feature representations yet identical free energy scores. This phenomenon occurs when the vector direction representing the feature space difference between the in-distribution and OOD sample lies within the null space of the last layer of a neural-based classifier. To mitigate these issues, we explore lower-dimensional feature spaces to reduce the null space footprint and introduce novel regularisation to maximize the least singular value of the final linear layer, hence enhancing inter-sample free energy separation. We refer to these techniques as Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection (FEVER-OOD). Our experiments show that FEVER-OOD techniques achieve state of the art OOD detection in Imagenet-100, with average OOD false positive rate (at 95% true positive rate) of 35.83% when used with the baseline Dream-OOD model.

Related papers

• Advancing Out-of-Distribution Detection via Local Neuroplasticity [60.53625435889467]
  This paper presents a novel OOD detection method that leverages the unique local neuroplasticity property of Kolmogorov-Arnold Networks (KANs) Our method compares the activation patterns of a trained KAN against its untrained counterpart to detect OOD samples. We validate our approach on benchmarks from image and medical domains, demonstrating superior performance and robustness compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-02-20T11:13:41Z)
• Can OOD Object Detectors Learn from Foundation Models? [56.03404530594071]
  Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models.
  arXiv  Detail & Related papers  (2024-09-08T17:28:22Z)
• 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)
• LINe: Out-of-Distribution Detection by Leveraging Important Neurons [15.797257361788812]
  We introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection.
  arXiv  Detail & Related papers  (2023-03-24T13:49:05Z)
• Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective [55.45202687256175]
  Out-of-distribution (OOD) detection methods assume that they have test ground truths, i.e., whether individual test samples are in-distribution (IND) or OOD. In this paper, we are the first to introduce the unsupervised evaluation problem in OOD detection. We propose three methods to compute Gscore as an unsupervised indicator of OOD detection performance.
  arXiv  Detail & Related papers  (2023-02-16T13:34:35Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• 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)
• A Simple Test-Time Method for Out-of-Distribution Detection [45.11199798139358]
  This paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection. We find that the probabilities of input images being out-of-distribution are surprisingly linearly correlated to the features extracted by neural networks. We propose an online variant of the proposed method, which achieves promising performance and is more practical in real-world applications.
  arXiv  Detail & Related papers  (2022-07-17T16:02:58Z)
• How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? [22.519572587827213]
  CIDER is a representation learning framework that exploits hyperspherical embeddings for OOD detection. CIDER establishes superior performance, outperforming the latest rival by 19.36% in FPR95.
  arXiv  Detail & Related papers  (2022-03-08T23:44:01Z)
• ReAct: Out-of-distribution Detection With Rectified Activations [20.792140933660075]
  Out-of-distribution (OOD) detection has received much attention lately due to its practical importance. One of the primary challenges is that models often produce highly confident predictions on OOD data. We propose ReAct--a simple and effective technique for reducing model overconfidence on OOD data.
  arXiv  Detail & Related papers  (2021-11-24T21:02:07Z)
• 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.