Describe how you would use Transfer Learning to improve a model's performance on a small dataset.

Instruction: Provide a detailed step-by-step approach, including the selection of the pre-trained model, the adaptation process, and any modifications to the model architecture or training process. Fictional case: 600 labeled industrial surface images from 60 parts; a natural-image pretrained encoder is available. Each part has ten views. Compare frozen-head training, partial fine-tuning and a simple baseline without leaking related views. Explain the staged training and acceptance decision.

Context: This question assesses the candidate's ability to leverage Transfer Learning effectively when data is scarce, showcasing their problem-solving skills and understanding of model adaptation and optimization.

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One example I would use is this: On a small dataset, I would start from a pretrained model that is as close as possible to the target domain, freeze most of the backbone at first, and...

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