SOURCE-LINKED INTELLIGENCE
Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems
Ensuring the reliability of deep learning-based image retrieval systems is a software engineering challenge. This paper presents a dual contribution: (1) a literature review of augmentation and generation techniques which resulted in the identification of 50 techniques which we organized into a ten-category taxonomy, and (2) a large-scale empirical study that evaluates these techniques as test generators for embedding-based image retrieval systems. Augmented images are embedded using Amazon Titan and OpenCLIP, and evaluated across four analytical dimensions: (1) embedding-space similarity, (2)
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T03:40:00.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.