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Image augmentation techniques tested as generators for deep learning image retrieval systems

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel approach to testing deep learning-based image retrieval systems by utilizing image augmentation techniques as test generators. The research categorizes 50 augmentation methods and empirically evaluates their effectiveness in generating diverse test cases. Experiments conducted on CIFAR-10, ImageNet-1K, and March Networks datasets, using Amazon Titan and OpenCLIP models, reveal that weather simulation and SaSPA techniques yield the highest embedding uncertainty and failure rates while maintaining realism. Conversely, GAN-based augmentations showed lower realism due to synthetic artifacts. AI

IMPACT Provides practical guidelines for selecting augmentation techniques to improve test diversity and realism for image retrieval systems.

RANK_REASON The item is a research paper detailing a new methodology for testing deep learning systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Image augmentation techniques tested as generators for deep learning image retrieval systems

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The item is a research paper detailing a new methodology for testing deep learning systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yehan De Silva, Anirudh Sridhar, Armin Lotfy, Nafiseh Kahani, Yvan Labiche, Ziyu Wang, Frank Ouyang, Clare Carty, Azalia Shamsaei ·

    Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems

    arXiv:2608.27502v1 Announce Type: cross Abstract: 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…

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