When data is scarce, training a deep CNN from scratch is likely to result in overfitting. " Transfer learning " allows an organization to take a model previously trained on a massive dataset (like ImageNet) and " fine-tune
" it for their specific, smaller dataset. The model already knows how to recognize basic shapes and textures, so it requires much less data to learn specific new categories. This is a common and highly effective practice in medical imaging or specialized manufacturing audits. Duplicating data (Option D) does not add new information and only leads to overfitting.