A similarity-based approach in few-shot learning is a method where models classify new data by comparing it to a small set of labeled examples, rather than relying on extensive training data. The core idea is to learn a function that measures how similar two data points are, then use this function to match unseen inputs to the most relevant examples in the support set (the small labeled dataset). This approach works well in scenarios where labeled data is scarce, as it avoids the need to train a model from scratch for every new task.
At a technical level, these approaches typically involve two stages. First, the model learns an embedding space where similar data points are clustered closer together, and dissimilar ones are farther apart. For example, in image classification, a model might map images to vectors such that pictures of the same animal (e.g., cats) are near each other in this space. During inference, the model computes the similarity between an unlabeled input and each example in the support set using metrics like cosine similarity or Euclidean distance. The class of the most similar support example is then assigned to the input. Methods like Prototypical Networks take this further by computing a “prototype” (average embedding) for each class in the support set and comparing the input to these prototypes, which improves efficiency.
A practical example is using a Siamese Network for signature verification. The network is trained on pairs of signatures, learning to output high similarity scores for genuine pairs and low scores for forgeries. In a few-shot setup, given a new signature to verify, the model compares it against a small set of reference signatures (e.g., five examples) and calculates similarity scores. The highest score determines whether the signature is authentic. This approach is efficient because the model doesn’t need retraining for new users—it generalizes by leveraging similarity comparisons. For developers, frameworks like PyTorch or TensorFlow provide tools to implement these models, often using pre-trained encoders to generate embeddings and custom loss functions (e.g., triplet loss) to optimize similarity metrics.