Transfer Learning

Transfer learning is the practice of taking a neural network that has already been trained on one large dataset and adapting it to perform well on a different, often smaller, related task. Instead of training a new network completely from scratch, which requires enormous amounts of data and computing power, transfer learning reuses the patterns a model has already learned, such as recognizing edges, shapes, and textures in images, and builds on top of that existing knowledge.

This approach has become extremely popular because training large networks from scratch is often impractical for individual developers or smaller companies without access to massive datasets or expensive computing infrastructure. Transfer learning makes advanced deep learning techniques accessible to a much wider range of projects, since a powerful pre-trained model can often be adapted to a new, specific task using a relatively small amount of additional data and training time.

What is a Pre-Trained Model?

A pre-trained model is a neural network that has already been trained on a large, general dataset, often containing millions of images or massive amounts of text, before being made available for others to use. These models have already learned a wide range of useful patterns during their original training, which can often be reused effectively for related tasks rather than learning everything again from zero.

For example, a model originally trained to classify a thousand different general categories of images has, in the process, learned to recognize a wide range of useful visual features like edges, textures, and shapes that are useful far beyond just the original thousand categories. Reusing this kind of pre-trained model as a starting point for a new, more specific task, such as classifying different types of medical scans, can save enormous amounts of time and data compared to training an entirely new network from scratch.

Fine-Tuning vs Feature Extraction

Popular Pre-Trained Models

1. Image Models

Popular pre-trained models for image tasks include:

• ResNet
• VGGNet
• MobileNet

These models were originally trained on large, general-purpose image datasets and are available in most deep learning frameworks, making them easy to download and use.

2. Language Models

For natural language tasks, developers commonly use pre-trained language models that have already been trained on massive amounts of text.

Training a language model from scratch requires significant computational resources, so transfer learning is the preferred approach for most projects.