Background Invariance Testing According to Semantic Proximity

TL;DR

This paper introduces a systematic method for testing background invariance in machine learning models by selecting semantically proximate scenes, improving test reliability over random or VLM-based sampling.

Contribution

It presents an ontology-based semantic proximity sampling approach for background invariance testing, enhancing diversity and consistency in testing ML models.

Findings

Semantic proximity sampling outperforms random sampling in test diversity.

Ontology-based scene selection improves testing consistency.

Enhanced reliability in background invariance evaluation.

Abstract

In many applications, machine-learned (ML) models are required to hold some invariance qualities, such as rotation, size, and intensity invariance. Among these, testing for background invariance presents a significant challenge due to the vast and complex data space it encompasses. To evaluate invariance qualities, we first use a visualization-based testing framework which allows human analysts to assess and make informed decisions about the invariance properties of ML models. We show that such informative testing framework is preferred as ML models with the same global statistics (e.g., accuracy scores) can behave differently and have different visualized testing patterns. However, such human analysts might not lead to consistent decisions without a systematic sampling approach to select representative testing suites. In this work, we present a technical solution for selecting background scenes according to their semantic proximity to a target image that contains a foreground object being tested. We construct an ontology for storing knowledge about relationships among different objects using association analysis. This ontology enables an efficient and meaningful search for background scenes of different semantic distances to a target image, enabling the selection of a test suite that is both diverse and reasonable. Compared with other testing techniques, e.g., random sampling, nearest neighbors, or other sampled test suites by visual-language models (VLMs), our method achieved a superior balance between diversity and consistency of human annotations, thereby enhancing the reliability and comprehensiveness of background invariance testing.