Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods

• URL: http://arxiv.org/abs/2407.07135v1
• Date: Tue, 9 Jul 2024 15:46:39 GMT
• Title: Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods
• Authors: Paul Novello, Yannick Prudent, Joseba Dalmau, Corentin Friedrich, Yann Pequignot,
• Abstract summary: Post-hoc deep Out-of-Distribution (OOD) detection has expanded rapidly. Current best practice is to test all the methods on the datasets at hand. This paper shifts focus from developing new methods to effectively combining existing ones to enhance OOD detection.
• Score: 1.747623282473278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since the seminal paper of Hendrycks et al. arXiv:1610.02136, Post-hoc deep Out-of-Distribution (OOD) detection has expanded rapidly. As a result, practitioners working on safety-critical applications and seeking to improve the robustness of a neural network now have a plethora of methods to choose from. However, no method outperforms every other on every dataset arXiv:2210.07242, so the current best practice is to test all the methods on the datasets at hand. This paper shifts focus from developing new methods to effectively combining existing ones to enhance OOD detection. We propose and compare four different strategies for integrating multiple detection scores into a unified OOD detector, based on techniques such as majority vote, empirical and copulas-based Cumulative Distribution Function modeling, and multivariate quantiles based on optimal transport. We extend common OOD evaluation metrics -- like AUROC and FPR at fixed TPR rates -- to these multi-dimensional OOD detectors, allowing us to evaluate them and compare them with individual methods on extensive benchmarks. Furthermore, we propose a series of guidelines to choose what OOD detectors to combine in more realistic settings, i.e. in the absence of known OOD data, relying on principles drawn from Outlier Exposure arXiv:1812.04606. The code is available at https://github.com/paulnovello/multi-ood.

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)
• MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities [11.884004583641325]
  We introduce the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset sizes and varying modality combinations. We first evaluate existing unimodal OOD detection algorithms on MultiOOD, observing that the mere inclusion of additional modalities yields substantial improvements. We introduce a novel outlier synthesis method, NP-Mix, which explores broader feature spaces by leveraging the information from nearest neighbor classes.
  arXiv  Detail & Related papers  (2024-05-27T17:59:02Z)
• Toward a Realistic Benchmark for Out-of-Distribution Detection [3.8038269045375515]
  We introduce a comprehensive benchmark for OOD detection based on ImageNet and Places365. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties.
  arXiv  Detail & Related papers  (2024-04-16T11:29:43Z)
• 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)
• Beyond AUROC & co. for evaluating out-of-distribution detection performance [50.88341818412508]
  Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. We propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples.
  arXiv  Detail & Related papers  (2023-06-26T12:51:32Z)
• BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection [0.43891501568660135]
  This paper introduces a novel method leveraging bi-encoder-based detectors. A comprehensive study comparing different out-of-distribution (OOD) detection methods in NLP is conducted. The proposed bi-encoder-based detectors outperform other methods, both those that require OOD labels in training and those that do not. The simplicity of the training process and the superior detection performance make them applicable to real-world scenarios.
  arXiv  Detail & Related papers  (2023-06-15T04:41:28Z)
• OpenOOD: Benchmarking Generalized Out-of-Distribution Detection [60.13300701826931]
  Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications. The field currently lacks a unified, strictly formulated, and comprehensive benchmark. We build a unified, well-structured called OpenOOD, which implements over 30 methods developed in relevant fields.
  arXiv  Detail & Related papers  (2022-10-13T17:59:57Z)
• 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)
• No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets [69.725266027309]
  Out-of-distribution detection is an important component of reliable ML systems. In this work, we show that none of these methods are inherently better at OOD detection than others on a standardized set of 16 pairs. We also show that a method outperforming another on a certain (ID, OOD) pair may not do so in a low-data regime.
  arXiv  Detail & Related papers  (2021-09-12T16:35:00Z)
• 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.