Which training method involves maintaining specific layers while others are updated?

The training method that involves maintaining specific layers while others are updated is indeed MoE-FT, which stands for Mixture of Experts Fine-Tuning. This technique typically allows for a subset of the model's parameters to remain static while others are fine-tuned. This selective updating enables practitioners to retain useful features learned by certain layers, particularly when adjusting a pre-trained model to a new task or dataset. By freezing certain layers, the model can maintain stability in the knowledge it has already gained while adapting other layers to potentially better fit the new information.

MoE-FT stands out from other methods mentioned because it is specifically concerned with selectively updating parts of a neural network rather than applying more general modifications to how a model learns or regulates data. The other methods—like Gradient Descent (which is a broad optimization algorithm), Dropout (which helps prevent overfitting by randomly deactivating neurons during training), and Batch Normalization (which normalizes layer inputs for stability in training)—do not incorporate the same approach of maintaining certain layers while updating others, thus differentiating MoE-FT in its operational mechanism within model training.