How does few-shot learning relate to deep learning?

Few-shot learning is a technique used in machine learning to train models effectively with very limited labeled data, and it is closely tied to deep learning because it relies on deep neural networks to achieve this. While traditional deep learning models require large datasets to generalize well, few-shot learning adapts these models to work with just a handful of examples. This is achieved by leveraging the representational power of deep neural networks, which can extract meaningful patterns from data even when training samples are scarce. Essentially, few-shot learning is a specialized application of deep learning that addresses scenarios where data collection is expensive or impractical.

Deep learning models excel at few-shot learning because their architectures can be designed or pretrained to capture general features that transfer across tasks. For example, a model might be pretrained on a large dataset (e.g., ImageNet for images or Wikipedia text for language tasks) to learn broad patterns. Then, during fine-tuning, the model uses this prior knowledge to adapt quickly to a new task with minimal examples. Techniques like metric learning, where the model learns to compare examples (e.g., Siamese networks), or meta-learning frameworks like Model-Agnostic Meta-Learning (MAML), which trains models to rapidly adjust parameters for new tasks, are common in few-shot setups. These approaches enable deep learning models to infer relationships between data points without relying on massive labeled datasets.

A practical example is image classification in medical imaging, where obtaining labeled data for rare diseases is challenging. A deep learning model pretrained on general medical images could be fine-tuned using just five labeled examples of a specific condition, relying on its pretrained feature extractors to identify relevant patterns. Similarly, in natural language processing, models like GPT-3 can perform tasks like translation or summarization with only a few examples provided in the input prompt, bypassing the need for task-specific training data. These examples highlight how few-shot learning builds on deep learning’s ability to generalize from pretraining while minimizing dependency on large labeled datasets—a critical advantage in resource-constrained domains.