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Linear probing freezes the foundation model and trains a head on top. In this method, we first make minor adjustments to just the final layer of the model, and then we perform more extensive training on the entire model. finetune和linearprobing是调整预训练模型以适应下游任务的策略。 finetune涉及对整个模型或部分模型进行参数更新，而linearprobing则保持模型参数不变，仅更新最后一层线性层，用于评估模型性能。 通常，linearprobing的性能低于finetune微调的结果。 finetune和linear probing一般和 预训练 搭配出现，是预训练模型适配下游任务时可选的训练方式. finetune是使用 预训练模型 适配下游任务时，对整个预训练模型全部进行参数更新. 微调也可以选择不进行全部更新，只对后面一部分模型进行更新，因为模型前几层一般提取的都是比较公共的特征，保留的底层信息较多，可以不用进行微调.

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While both techniques aim to improve model performance, they differ in their approach and characteristics.