海上物体分類のためのロバストな不確実性推定
Robust Uncertainty Estimation for Classification of Maritime Objects
モンテカルロドロップアウトと外れ値検出を組み合わせ、海上物体分類の不確実性を高精度に推定する手法を提案し、新データセットSHIPSで有効性を示した。
著者: Jonathan Becktor, Frederik Scholler, Evangelos Boukas, Lazaros Nalpantidis
分類: cs.LG, cs.RO
原文アブストラクト
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.
