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Surgical Fine-Tuning for Adaptation by miranijunaid423 is a document available to read on EtoBox.

What is Surgical Fine-Tuning for Adaptation about?

This paper introduces surgical fine-tuning, a method that selectively fine-tunes a subset of layers in a pre-trained model to improve adaptation to distribution shifts, showing that it can outperform traditional fine-tuning methods. The effectiveness of the layers to tune varies based on the type of distribution shift, with early layers being more effective for input-level shifts and later layers for output-level shifts. The findings are validated through empirical experiments across seven real-world tasks

Author

miranijunaid423

Language

EN