Transfer learning in Convolutional Neural Networks

For our day to day activities related to computer vision ,Instead of training a CNN from scratch, transfer learning enables us to utilize the knowledge and learned representations of a pre-trained model, which has already been trained on a vast amount of data, typically from a related task.

So , how does transfer learning works??

From pre-trained models , Feature extraction happens on which Fine tuning is done as per the requirement and at last the final layers are trained. I have mentioned in detail in the below chart .

Benefits of transfer learning in CNN:

• Reduced training time and computational resources: Training a CNN from scratch on large datasets can be computationally expensive. Transfer learning enables us to leverage pre-trained models, saving significant time and resources.
• Improved performance with limited data: By utilizing pre-trained models, we can overcome the data scarcity problem. The pre-trained models have learned generic features from large-scale datasets, which can be beneficial when working with limited labeled data.
• Generalization to new tasks: Transfer learning allows us to transfer knowledge learned from one task to another. The features learned from a large dataset are often general enough to be applicable to various related tasks.