Initial Training Image Size Effects on CNN Accuracy-Efficiency Trade-offs Across Domains

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the initial training image size affect the trade-off between accuracy and training efficiency in state-of-the-art CNNs (e.g., EfficientNet, Vision Transformers) when trained on mixed-domain. 8 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the initial training image size affect the trade-off between accuracy and training efficiency in state-of-the-art CNNs (e.g., EfficientNet, Vision Transformers) when trained on mixed-domain datasets, as measured by HLCE benchmark accuracy and training time?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance