How does few-shot learning differ from transfer learning?

Few-shot learning and transfer learning are both techniques to improve machine learning models with limited data, but they approach the problem differently. Few-shot learning focuses on training models to recognize new classes or tasks using very few examples—often just one to five samples per category. Transfer learning, in contrast, involves adapting a model pre-trained on a large source dataset to a new target task by fine-tuning it with a smaller, task-specific dataset. The key difference lies in their data requirements and how they leverage prior knowledge: transfer learning assumes a moderate-sized target dataset exists for fine-tuning, while few-shot learning aims to generalize from almost no labeled data.

In transfer learning, a model first learns general features from a large, diverse dataset (e.g., ImageNet for vision or Wikipedia text for language models). This pre-trained model is then fine-tuned on a smaller dataset for a specific task, like classifying medical images or analyzing customer reviews. For example, a ResNet model trained on ImageNet can be adapted to detect pneumonia in X-rays by retraining its final layers on a few thousand medical images. Few-shot learning, however, skips the fine-tuning step or minimizes it. Instead, the model is designed to infer new tasks directly from its pre-existing knowledge. For instance, GPT-3 can perform text classification with just a few examples provided in a prompt, relying on patterns learned during pre-training rather than explicit retraining. Techniques like metric learning (e.g., Siamese networks) or meta-learning (e.g., Model-agnostic Meta-Learning) are often used to teach models to compare or adapt to new tasks quickly.

The choice between the two depends on data availability and task specificity. Transfer learning is practical when you have at least hundreds of labeled examples for the target task, as seen in customizing BERT for sentiment analysis using a domain-specific dataset. Few-shot learning shines when labeling data is expensive or impractical, such as classifying rare animal species with only a handful of images. A practical example is using a pre-trained vision model like CLIP, which can classify unseen objects by comparing them to text descriptions without additional training. While transfer learning requires infrastructure for fine-tuning, few-shot learning prioritizes immediate adaptability, often at the cost of lower performance on highly specialized tasks. Developers should evaluate their data constraints and whether the target task aligns with the pre-training domain to decide which approach fits best.