Comprehensive OOD Detection Improvements

• URL: http://arxiv.org/abs/2401.10176v1
• Date: Thu, 18 Jan 2024 18:05:35 GMT
• Title: Comprehensive OOD Detection Improvements
• Authors: Anish Lakkapragada, Amol Khanna, Edward Raff, Nathan Inkawhich
• Abstract summary: Out-of-distribution (OOD) detection methods have been created for this task. We employ dimensionality reduction on feature embeddings in representation-based methods for both time speedups and improved performance. We demonstrate the effectiveness of our methods on the OpenOODv1.5 benchmark framework.
• Score: 46.46252643210326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for giving context to predictions. Out-of-distribution (OOD) detection methods have been created for this task. Such methods can be split into representation-based or logit-based methods from whether they respectively utilize the model's embeddings or predictions for OOD detection. In contrast to most papers which solely focus on one such group, we address both. We employ dimensionality reduction on feature embeddings in representation-based methods for both time speedups and improved performance. Additionally, we propose DICE-COL, a modification of the popular logit-based method Directed Sparsification (DICE) that resolves an unnoticed flaw. We demonstrate the effectiveness of our methods on the OpenOODv1.5 benchmark framework, where they significantly improve performance and set state-of-the-art results.

Related papers

• Margin-bounded Confidence Scores for Out-of-Distribution Detection [2.373572816573706]
  We propose a novel method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem. MaCS enlarges the disparity between ID and OOD scores, which in turn makes the decision boundary more compact. Experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-09-22T05:40:25Z)
• FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning [0.0]
  We introduce textitFlowCon, a new density-based OOD detection technique. Our main innovation lies in efficiently combining the properties of normalizing flow with supervised contrastive learning. Empirical evaluation shows the enhanced performance of our method across common vision datasets.
  arXiv  Detail & Related papers  (2024-07-03T20:33:56Z)
• WeiPer: OOD Detection using Weight Perturbations of Class Projections [11.130659240045544]
  We introduce perturbations of the class projections in the final fully connected layer which creates a richer representation of the input. We achieve state-of-the-art OOD detection results across multiple benchmarks of the OpenOOD framework.
  arXiv  Detail & Related papers  (2024-05-27T13:38:28Z)
• 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)
• Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities [104.02531442035483]
  The goal of this paper is to recognize common objectives as well as to identify the implicit scoring functions of different OOD detection methods. We show that binary discrimination between in- and (different) out-distributions is equivalent to several distinct formulations of the OOD detection problem. We also show that the confidence loss which is used by Outlier Exposure has an implicit scoring function which differs in a non-trivial fashion from the theoretically optimal scoring function.
  arXiv  Detail & Related papers  (2022-06-20T16:32:49Z)
• How Useful are Gradients for OOD Detection Really? [5.459639971144757]
  Out of distribution (OOD) detection is a critical challenge in deploying highly performant machine learning models in real-life applications. We provide an in-depth analysis and comparison of gradient based methods for OOD detection. We propose a general, non-gradient based method of OOD detection which improves over previous baselines in both performance and computational efficiency.
  arXiv  Detail & Related papers  (2022-05-20T21:10:05Z)
• 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)
• On the Practicality of Deterministic Epistemic Uncertainty [106.06571981780591]
  deterministic uncertainty methods (DUMs) achieve strong performance on detecting out-of-distribution data. It remains unclear whether DUMs are well calibrated and can seamlessly scale to real-world applications.
  arXiv  Detail & Related papers  (2021-07-01T17:59:07Z)
• Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU [70.44344060176952]
  Intent classification is a major task in spoken language understanding (SLU) Recent works have shown that using extra data and labels can improve the OOD detection performance. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection.
  arXiv  Detail & Related papers  (2021-06-28T08:27:38Z)
• 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.