A common architecture used in few-shot learning is metric-based learning, which focuses on comparing data points in a way that makes similar examples cluster together and dissimilar ones separate. This approach avoids complex model architectures and instead relies on designing effective distance metrics. For example, Prototypical Networks are a popular choice: they compute a “prototype” (average feature vector) for each class using the few available examples. New instances are classified by measuring their distance to these prototypes, often using Euclidean or cosine distance. This method is efficient because it doesn’t require retraining the entire model for new tasks, making it practical for scenarios with limited data. Developers can implement this using standard neural networks with a custom loss function that enforces similarity between examples of the same class.
Another widely used architecture is meta-learning frameworks like Model-Agnostic Meta-Learning (MAML). MAML trains a model on a variety of tasks during meta-training, optimizing its initial parameters so it can adapt quickly to new tasks with minimal updates. For instance, if training a model to recognize animal species, MAML would expose the model to many classification tasks (e.g., birds vs. fish, cats vs. dogs) during meta-training. When faced with a new task (e.g., distinguishing rare insects), the model can fine-tune its pre-trained parameters using just a few examples. This approach is flexible because it works with any gradient-based model architecture, such as CNNs or transformers, and requires only minor code adjustments to existing training loops.
Lastly, transformer-based models adapted with prompt engineering have gained traction in few-shot learning. Pretrained language models like GPT-3 or BERT can perform tasks with minimal examples by reformulating the task as a text completion problem. For example, to classify sentiment, a developer might provide a prompt like “The movie was thrilling. Sentiment: positive. The food was cold. Sentiment: negative. The play was boring. Sentiment: ___.” The model uses its pretrained knowledge to fill in the blank. While not a traditional “architecture,” this method leverages the transformer’s ability to generalize from context. Developers can implement it using APIs or libraries like Hugging Face’s Transformers, with minimal fine-tuning required. These approaches—metric-based learning, meta-learning, and prompt-driven transformers—provide practical, flexible solutions for few-shot problems.