In transfer learning, all layers of the pre-trained model are always frozen during fine-tuning.Group of answer choicesTrueFalse

In transfer learning, it is not always true that all layers of the pre-trained model are frozen during fine-tuning.

Explanation

1. Freezing Layers: In many transfer learning scenarios, the earlier layers of the model, which capture more generic features, may be frozen (not updated during training) while the later layers are fine-tuned to adapt to the specific task at hand.

2. Unfreezing Layers: Conversely, some approaches allow for unfreezing certain layers, especially if the target dataset is large enough or if the model needs to learn specific features that are not captured by the pre-trained weights.

Conclusion

Thus, the correct answer is False. It's common practice to selectively freeze and unfreeze layers during the fine-tuning process in transfer learning.