Data Augmentation vs. Transfer Learning for Small-Scale Dataset Generalization

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent do data augmentation strategies improve the generalization of deep learning models on small-scale datasets compared to transfer learning from large-scale pre-trained weights. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent do data augmentation strategies improve the generalization of deep learning models on small-scale datasets compared to transfer learning from large-scale pre-trained weights?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.