Pre-Trained Neural Network
In transfer learning, the choice of which layer to use as a feature extractor in a pre-trained neural network depends on several factors:
Type of neural network:
- Convolutional Neural Networks (CNNs): For CNNs in image classification, earlier layers tend to extract generic features like edges and textures, while later layers extract more task-specific features like object parts. Early layers (e.g., Conv1-3) are often used for general feature extraction, while middle layers might be suitable for tasks with higher-level similarities to the original task.
- Recurrent Neural Networks (RNNs): For RNNs like LSTMs in text processing, earlier layers capture basic temporal patterns, while later layers encode more complex sequences. The choice depends on the level of abstraction needed for the new task.
Similarity of tasks:
- Highly similar tasks: If the new task is very similar to the original task, using later layers may be more effective as they have already learned relevant features.
- Dissimilar tasks: For dissimilar tasks, using earlier layers with more general features might be better, as later layers may be too specific to the original task.
Fine-tuning vs. freezing:
- Fine-tuning: If you plan to fine-tune some layers of the pre-trained model, choosing layers closer to the output may be appropriate, as they are more likely to adapt to the new task.
- Freezing: If you plan to freeze most of the pre-trained model, using earlier layers is more common, as they are less likely to be disrupted by the new training data.
Here are some general guidelines:
- For image classification, early layers (Conv1-3) are often good starting points.
- For text processing with LSTMs, consider both early and middle layers depending on the task complexity.
- Start with a general layer and experiment with different choices to find the best performance for your specific task.
Ultimately, the best way to determine which layer to use is to experiment and compare the performance of different options on your specific dataset and task.
