Jan 7, 2024 · In summary, fine-tuning and linear probing are two popular techniques for adapting pre-trained models to new tasks. While fine-tuning allows for adaptation to new data, it can distort pre …

Initially, linear probing (LP) optimizes only the linear head of the model, after which fine-tuning (FT) updates the entire model, including the feature extractor and the linear head.

Sep 25, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both in-distribution (ID) and out-of …

Apr 5, 2023 · Most of my work focuses on coming up with better algorithms to pre-train foundation models, and how do you actually fine-tune or use these foundation models better—especially when …

Jan 28, 2025 · The method adopts a two-stage strategy: in the first stage, the linear head of the model is trained using linear probing; in the second stage, fine-tuning update the entire model following the …

Oct 23, 2024 · The study examines the relationship between the model's feature space during linear probing and the optimization trajectory during fine-tuning. They show that linear probing creates an …

Sep 26, 2024 · It demonstrates that linear probing then fine-tuning (LP-FT) and LoRA methods lead to smaller changes in pre-trained features while significantly increasing the classifier norm compared to …

Jul 8, 2025 · 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 …

The research analyzes the training dynamics of linear probing and full fine-tuning within differentially private settings. It examines the sequential fine-tuning process and its implications on test loss.

1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). Changes to pre-trained features are minimized. Problem: Existing analyses focus on two-layer linear …