Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing
Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz

TL;DR
This paper introduces an adaptive hierarchical certification method for segmentation that reduces abstain rates and provides more meaningful semantic information by certifying pixels at multiple levels of class granularity, improving safety in critical applications.
Contribution
It proposes a novel hierarchical certification framework with an adaptive algorithm that relaxes certification levels, along with a new metric, CIG, to measure information gain, validated on multiple datasets.
Findings
Higher Certified Information Gain (CIG) achieved
Lower abstain rates compared to state-of-the-art methods
Effective certification across multiple datasets
Abstract
Certification for machine learning is proving that no adversarial sample can evade a model within a range under certain conditions, a necessity for safety-critical domains. Common certification methods for segmentation use a flat set of fine-grained classes, leading to high abstain rates due to model uncertainty across many classes. We propose a novel, more practical setting, which certifies pixels within a multi-level hierarchy, and adaptively relaxes the certification to a coarser level for unstable components classic methods would abstain from, effectively lowering the abstain rate whilst providing more certified semantically meaningful information. We mathematically formulate the problem setup, introduce an adaptive hierarchical certification algorithm and prove the correctness of its guarantees. Since certified accuracy does not take the loss of information into account for coarser…
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Taxonomy
TopicsFace and Expression Recognition · Machine Learning and Data Classification · Industrial Vision Systems and Defect Detection
MethodsSparse Evolutionary Training
