Advancing the CNN classifier with transfer learning
Transfer learning is a machine learning technique where a model trained on one task is adapted or fine-tuned for a second related task. In transfer learning, the knowledge acquired during the training of the first task (source task) is leveraged to improve the learning of the second task (target task). This can be particularly useful when you have limited data for the target task because it allows you to transfer knowledge from a larger or more diverse dataset.
The typical workflow of transfer learning involves:
- Pretrained model: Start with a pretrained model that has already been trained on a large and relevant dataset for a different but related task. This model is often a deep neural network, such as a CNN model for image tasks.
- Feature Extraction: Use the pretrained model as a feature extractor. Remove the final classification layers (if they exist) and use the output of one of the intermediate layers as...
