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Robust Uncertainty Estimation for Classification of Maritime Objects

arXiv 2307.01325 ↗First posted 3 Jul 2023cs.LG cs.ROcited by 0

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Abstract (arXiv)

We explore the use of uncertainty estimation in the maritime domain, showing the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset, SHIPS. We present a method joining the intra-class uncertainty achieved using Monte Carlo Dropout, with recent discoveries in the field of outlier detection, to gain more holistic uncertainty measures. We explore the relationship between the introduced uncertainty measures and examine how well they work on CIFAR10 and in a real-life setting. Our work improves the FPR95 by 8% compared to the current highest-performing work when the models are trained without out-of-distribution data. We increase the performance by 77% compared to a vanilla implementation of the Wide ResNet. We release the SHIPS dataset and show the effectiveness of our method by improving the FPR95 by 44.2% with respect to the baseline. Our approach is model agnostic, easy to implement, and often does not require model retraining.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 3 Jul 2023.

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24 of 40 references.

References · 24

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
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12 more
  1. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
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  3. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  4. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
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  11. 2020Dense open-set recognition with synthetic outliers generated by Real NVPcited by 29
  12. 2021An Effective Baseline for Robustness to Distributional Shiftcited by 25

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