Transfer Learning Explained: How Transfer Learning Transforms Neural Network Training

 What is Transfer Learning?

Transfer Learning is a machine learning technique that leverages knowledge gained from solving one problem to improve learning in a related task. This approach is analogous to human learning, where we apply previously acquired skills to new but similar situations.

Bike Analogy

Consider learning to ride a bicycle. The skills you develop—balancing, coordinating body movements, and understanding traffic rules—can be transferred when learning to ride a motorcycle. Some skills, like pedaling, are specific to bicycles and are not needed for motorcycles. Similarly, in neural networks, we aim to reuse generic layers while adapting or replacing task-specific layers.

Why is Transfer Learning Important?

Transfer Learning addresses several challenges in deep learning:

• Data Scarcity: It enables effective learning with limited datasets.
• Computational Efficiency: It reduces the time and resources required for training.
• Performance Boost: It often leads to improved model performance on new tasks.
• Faster Development: It accelerates the development of models for new applications.

How Transfer Learning Works

The process typically involves the following steps:

1. Select a pre-trained model relevant to your task
2. Freeze some or all layers of the pre-trained model
3. Add new layers specific to your task
4. Train the model on your dataset, updating only the new layers
5. Optionally, fine-tune the entire model with a low learning rate

The Pre-trained model has multiple Convolution and Pooling layers , lets look at them

Convolutional Layers

Convolutional layers are the core building blocks of CNNs. They apply learned filters to input data to extract features . Layers have activation functions that use element-wise multiplication and summation

Pooling Layers

Pooling layers follow convolutional layers and downsample the feature maps. They reduce dimensions and minimize the computational load .It may be of Max or Average pooling .Pooling layers provide capability to translational variance and reduces over fitting

Transfer Learning with Pre-trained Models

Transfer learning allows leveraging knowledge from pre-trained models to new tasks. Initial layers from the Pre-trained model are not modified (Frozen) and the output and classification layers are dropped to be retrained .Using Image Classification as the example initial layers pickup patterns, edges and shapes that is useful for classification of any image and the layers close to the output will be responsible for classification and needs to be retrained .

By leveraging pre-trained convolutional and pooling layers through transfer learning, you can significantly improve the efficiency and effectiveness of training models for new tasks, especially when working with limited datasets.

Example 

I have used Transfer Learning for Classification of Flowers . You can find the example here . The initial model is trained for four types of flowers and then  model is used to train for new type of flower . 

Image classification is highly used in Medical field for analyzing X-rays and MRIs

It is also multiple other uses in the field of NLP , Speech Recognition ,Computer Vision and Bioinformatics.