Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy

• URL: http://arxiv.org/abs/2305.11616v5
• Date: Tue, 05 Nov 2024 10:41:42 GMT
• Title: Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
• Authors: Stanislav Dereka, Ivan Karpukhin, Maksim Zhdanov, Sergey Kolesnikov,
• Abstract summary: Saliency Diversified Deep Ensemble (SDDE) is a novel approach that promotes diversity among ensemble members by leveraging saliency maps. In particular, the proposed method achieves state-of-the-art OOD detection quality, calibration, and accuracy on multiple benchmarks, including CIFAR10/100 and large-scale ImageNet datasets.
• Score: 5.62479170374811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homogeneity of learned patterns within ensembles. To overcome this issue, our study introduces Saliency Diversified Deep Ensemble (SDDE), a novel approach that promotes diversity among ensemble members by leveraging saliency maps. Through incorporating saliency map diversification, our method outperforms conventional ensemble techniques and improves calibration in multiple classification and OOD detection tasks. In particular, the proposed method achieves state-of-the-art OOD detection quality, calibration, and accuracy on multiple benchmarks, including CIFAR10/100 and large-scale ImageNet datasets.

Related papers

• Scalable Ensemble Diversification for OOD Generalization and Detection [68.8982448081223]
  SED identifies hard training samples on the fly and encourages the ensemble members to disagree on these. We show how to avoid the expensive computations in existing methods of exhaustive pairwise disagreements across models. For OOD generalization, we observe large benefits from the diversification in multiple settings including output-space (classical) ensembles and weight-space ensembles (model soups)
  arXiv  Detail & Related papers  (2024-09-25T10:30:24Z)
• Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks [17.520137576423593]
  We aim to provide a consolidated view of the two largest sub-fields within the community: out-of-distribution (OOD) detection and open-set recognition (OSR) We perform rigorous cross-evaluation between state-of-the-art methods in the OOD detection and OSR settings and identify a strong correlation between the performances of methods for them. We propose a new, large-scale benchmark setting which we suggest better disentangles the problem tackled by OOD detection and OSR.
  arXiv  Detail & Related papers  (2024-08-29T17:55:07Z)
• Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods [1.747623282473278]
  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.
  arXiv  Detail & Related papers  (2024-07-09T15:46:39Z)
• 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)
• 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)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• 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)
• Deep Combinatorial Aggregation [58.78692706974121]
  Deep ensemble is a simple and effective method that achieves state-of-the-art results for uncertainty-aware learning tasks. In this work, we explore a generalization of deep ensemble called deep aggregation (DCA) DCA creates multiple instances of network components and aggregates their combinations to produce diversified model proposals and predictions.
  arXiv  Detail & Related papers  (2022-10-12T17:35:03Z)
• On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection [7.221206118679026]
  The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications of deep learning. An existing intuition in the literature is that the diversity of Deep Ensemble predictions indicates distributional shift. We show experimentally that this intuition is not valid on ImageNet-scale OOD detection.
  arXiv  Detail & Related papers  (2022-07-15T15:02:38Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data [77.27338842609153]
  characterization reveals that sensitivity to each type of shift is important to the detection and confidence calibration of OOD data. We propose a geometrically-inspired method to improve OOD detection under both shifts with only in-distribution data. We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts.
  arXiv  Detail & Related papers  (2021-10-28T15:42:55Z)

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.