AI agents leverage transfer learning by using knowledge gained from solving one problem to improve performance on a related but different task. Instead of training a model from scratch, developers start with a pre-trained model that has already learned general patterns from a large dataset. This approach is especially useful when the target task has limited data. For example, a model trained on general image recognition (like identifying cars or animals) can be adapted to diagnose medical images by fine-tuning its last few layers on a smaller dataset of X-rays. The pre-trained layers handle basic feature detection (edges, textures), while the task-specific layers learn to recognize domain-specific patterns (e.g., tumors).
The process typically involves reusing the early layers of a neural network, which capture universal features, and retraining the later layers on the new task. For instance, in natural language processing (NLP), models like BERT or GPT, pre-trained on vast text corpora, can be adapted for sentiment analysis or question-answering by updating the final classification layers. Frameworks like TensorFlow and PyTorch simplify this by allowing developers to “freeze” certain layers during training. Freezing prevents the weights of pre-trained layers from changing, reducing computational costs and overfitting risks. For example, a developer might freeze the first 80% of a vision model’s layers when adapting it to recognize specific industrial defects in images, focusing training effort on the remaining layers tailored to the new data.
The benefits of transfer learning include faster training, reduced data requirements, and improved performance in specialized domains. A practical example is using a ResNet model pre-trained on ImageNet to classify plant diseases with only a few hundred labeled images instead of millions. However, success depends on the similarity between the source and target tasks. If the tasks are too different—like using a speech recognition model for fraud detection—transfer learning may offer little advantage. Developers must also balance how much of the model to retrain: too little adaptation leads to poor task alignment, while retraining too many layers risks losing useful pre-trained features. Tools like Keras’s include_top=False option or Hugging Face’s AutoModel APIs help manage this balance, enabling efficient customization for specific use cases.