What is transfer learning in deep learning?

Transfer learning in deep learning is a technique where a model developed for one task is reused as a starting point for a different but related task. Instead of training a model from scratch, you leverage knowledge (like learned features or patterns) from a pre-trained model and adapt it to your specific problem. This approach is particularly useful when you have limited data for your target task, as the pre-trained model already encapsulates generalizable features from a larger dataset. For example, a model trained to recognize objects in images (like cats or cars) can be repurposed to identify medical anomalies in X-rays by retraining only a portion of its layers.

The core idea behind transfer learning is that neural networks learn hierarchical representations of data. Lower layers in a model typically capture basic patterns (edges, textures in images; word embeddings in text), while higher layers capture task-specific details. By freezing the initial layers of a pre-trained model and retraining only the later layers, you retain the general features while tailoring the model to your specific task. For instance, in natural language processing (NLP), a language model like BERT, pre-trained on vast text corpora, can be fine-tuned for sentiment analysis by adding a classification layer and training it on a smaller dataset of labeled reviews. This avoids the computational cost of training a massive model from scratch.

Practical examples of transfer learning are widespread. In computer vision, frameworks like TensorFlow and PyTorch provide pre-trained models such as ResNet or MobileNet, which are often used for tasks like custom image classification. For example, a developer building a plant disease detector could start with ResNet-50 (trained on ImageNet), replace its final classification layer, and retrain it on a smaller dataset of plant images. Similarly, in NLP, libraries like Hugging Face Transformers offer models like GPT or RoBERTa, which can be adapted for tasks like text summarization or named entity recognition. Transfer learning also applies to domains like healthcare, where models trained on general medical images can be adapted for rare diseases with limited data. Tools like Keras’ applications module simplify this process by allowing developers to load pre-trained weights and modify only the necessary parts of the model architecture.