Question 10Why does transfer learning work?1 pointAll layers of filters can be learned by studying the mammalian receptive fields.All images are composed of pixels with three color channels.Low-level features are specialized for a particular task, while top-level features are universal to all images.Top-level features are specialized for a particular task, while low-level features are universal to all images

Transfer learning works primarily because of the last option: "Top-level features are specialized for a particular task, while low-level features are universal to all images."

Here's a step-by-step explanation:

1. Low-level features: These are basic features like edges, textures, and colors that are common in all images, regardless of the specific task. These features are usually learned in the initial layers of a deep learning model. Since these features are universal, a model trained on one task can effectively transfer this knowledge to another task.

2. Top-level features: These are more complex and task-specific features that the model learns in the later layers. For example, a model trained to recognize animals might learn to identify shapes of ears, noses, and eyes in its top layers.

3. Transfer learning: In transfer learning, we leverage the knowledge (weights) a model has learned from a previous task (usually a larger and more general one) to a new task (usually a smaller and more specific one). The idea is to freeze the initial layers that have learned the low-level features and retrain the later layers to learn the new task-specific features. This works because the low-level features are common across tasks, and only the top-level features need to be adapted to the new task.

4. Efficiency: Transfer learning is efficient because it reduces the amount of data needed to train a model and often results in better performance, especially when the new task is similar to the original task the model was trained on.